{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实战练习之一 AI股票拟合算法"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输入必要的库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas            as pd\n",
    "import tensorflow        as tf  \n",
    "import numpy             as np\n",
    "import matplotlib.pyplot as plt\n",
    "# 支持中文\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "from numpy                 import array\n",
    "from sklearn               import metrics\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras.models          import Sequential\n",
    "from tensorflow.keras.layers          import Dense,LSTM,Bidirectional\n",
    "\n",
    "from numpy.random   import seed\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ALLOW_THREADS',\n",
       " 'BUFSIZE',\n",
       " 'CLIP',\n",
       " 'DataSource',\n",
       " 'ERR_CALL',\n",
       " 'ERR_DEFAULT',\n",
       " 'ERR_IGNORE',\n",
       " 'ERR_LOG',\n",
       " 'ERR_PRINT',\n",
       " 'ERR_RAISE',\n",
       " 'ERR_WARN',\n",
       " 'FLOATING_POINT_SUPPORT',\n",
       " 'FPE_DIVIDEBYZERO',\n",
       " 'FPE_INVALID',\n",
       " 'FPE_OVERFLOW',\n",
       " 'FPE_UNDERFLOW',\n",
       " 'False_',\n",
       " 'Inf',\n",
       " 'Infinity',\n",
       " 'MAXDIMS',\n",
       " 'MAY_SHARE_BOUNDS',\n",
       " 'MAY_SHARE_EXACT',\n",
       " 'NAN',\n",
       " 'NINF',\n",
       " 'NZERO',\n",
       " 'NaN',\n",
       " 'PINF',\n",
       " 'PZERO',\n",
       " 'RAISE',\n",
       " 'RankWarning',\n",
       " 'SHIFT_DIVIDEBYZERO',\n",
       " 'SHIFT_INVALID',\n",
       " 'SHIFT_OVERFLOW',\n",
       " 'SHIFT_UNDERFLOW',\n",
       " 'ScalarType',\n",
       " 'True_',\n",
       " 'UFUNC_BUFSIZE_DEFAULT',\n",
       " 'UFUNC_PYVALS_NAME',\n",
       " 'WRAP',\n",
       " '_CopyMode',\n",
       " '_NoValue',\n",
       " '_UFUNC_API',\n",
       " '__NUMPY_SETUP__',\n",
       " '__all__',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__config__',\n",
       " '__deprecated_attrs__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__expired_functions__',\n",
       " '__file__',\n",
       " '__former_attrs__',\n",
       " '__future_scalars__',\n",
       " '__getattr__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " '_add_newdoc_ufunc',\n",
       " '_builtins',\n",
       " '_core',\n",
       " '_distributor_init',\n",
       " '_financial_names',\n",
       " '_get_promotion_state',\n",
       " '_globals',\n",
       " '_int_extended_msg',\n",
       " '_mat',\n",
       " '_no_nep50_warning',\n",
       " '_pyinstaller_hooks_dir',\n",
       " '_pytesttester',\n",
       " '_set_promotion_state',\n",
       " '_specific_msg',\n",
       " '_typing',\n",
       " '_using_numpy2_behavior',\n",
       " '_utils',\n",
       " 'abs',\n",
       " 'absolute',\n",
       " 'add',\n",
       " 'add_docstring',\n",
       " 'add_newdoc',\n",
       " 'add_newdoc_ufunc',\n",
       " 'all',\n",
       " 'allclose',\n",
       " 'alltrue',\n",
       " 'amax',\n",
       " 'amin',\n",
       " 'angle',\n",
       " 'any',\n",
       " 'append',\n",
       " 'apply_along_axis',\n",
       " 'apply_over_axes',\n",
       " 'arange',\n",
       " 'arccos',\n",
       " 'arccosh',\n",
       " 'arcsin',\n",
       " 'arcsinh',\n",
       " 'arctan',\n",
       " 'arctan2',\n",
       " 'arctanh',\n",
       " 'argmax',\n",
       " 'argmin',\n",
       " 'argpartition',\n",
       " 'argsort',\n",
       " 'argwhere',\n",
       " 'around',\n",
       " 'array',\n",
       " 'array2string',\n",
       " 'array_equal',\n",
       " 'array_equiv',\n",
       " 'array_repr',\n",
       " 'array_split',\n",
       " 'array_str',\n",
       " 'asanyarray',\n",
       " 'asarray',\n",
       " 'asarray_chkfinite',\n",
       " 'ascontiguousarray',\n",
       " 'asfarray',\n",
       " 'asfortranarray',\n",
       " 'asmatrix',\n",
       " 'atleast_1d',\n",
       " 'atleast_2d',\n",
       " 'atleast_3d',\n",
       " 'average',\n",
       " 'bartlett',\n",
       " 'base_repr',\n",
       " 'binary_repr',\n",
       " 'bincount',\n",
       " 'bitwise_and',\n",
       " 'bitwise_not',\n",
       " 'bitwise_or',\n",
       " 'bitwise_xor',\n",
       " 'blackman',\n",
       " 'block',\n",
       " 'bmat',\n",
       " 'bool_',\n",
       " 'broadcast',\n",
       " 'broadcast_arrays',\n",
       " 'broadcast_shapes',\n",
       " 'broadcast_to',\n",
       " 'busday_count',\n",
       " 'busday_offset',\n",
       " 'busdaycalendar',\n",
       " 'byte',\n",
       " 'byte_bounds',\n",
       " 'bytes_',\n",
       " 'c_',\n",
       " 'can_cast',\n",
       " 'cast',\n",
       " 'cbrt',\n",
       " 'cdouble',\n",
       " 'ceil',\n",
       " 'cfloat',\n",
       " 'char',\n",
       " 'character',\n",
       " 'chararray',\n",
       " 'choose',\n",
       " 'clip',\n",
       " 'clongdouble',\n",
       " 'clongfloat',\n",
       " 'column_stack',\n",
       " 'common_type',\n",
       " 'compare_chararrays',\n",
       " 'compat',\n",
       " 'complex128',\n",
       " 'complex64',\n",
       " 'complex_',\n",
       " 'complexfloating',\n",
       " 'compress',\n",
       " 'concatenate',\n",
       " 'conj',\n",
       " 'conjugate',\n",
       " 'convolve',\n",
       " 'copy',\n",
       " 'copysign',\n",
       " 'copyto',\n",
       " 'corrcoef',\n",
       " 'correlate',\n",
       " 'cos',\n",
       " 'cosh',\n",
       " 'count_nonzero',\n",
       " 'cov',\n",
       " 'cross',\n",
       " 'csingle',\n",
       " 'ctypeslib',\n",
       " 'cumprod',\n",
       " 'cumproduct',\n",
       " 'cumsum',\n",
       " 'datetime64',\n",
       " 'datetime_as_string',\n",
       " 'datetime_data',\n",
       " 'deg2rad',\n",
       " 'degrees',\n",
       " 'delete',\n",
       " 'deprecate',\n",
       " 'deprecate_with_doc',\n",
       " 'diag',\n",
       " 'diag_indices',\n",
       " 'diag_indices_from',\n",
       " 'diagflat',\n",
       " 'diagonal',\n",
       " 'diff',\n",
       " 'digitize',\n",
       " 'disp',\n",
       " 'divide',\n",
       " 'divmod',\n",
       " 'dot',\n",
       " 'double',\n",
       " 'dsplit',\n",
       " 'dstack',\n",
       " 'dtype',\n",
       " 'dtypes',\n",
       " 'e',\n",
       " 'ediff1d',\n",
       " 'einsum',\n",
       " 'einsum_path',\n",
       " 'emath',\n",
       " 'empty',\n",
       " 'empty_like',\n",
       " 'equal',\n",
       " 'errstate',\n",
       " 'euler_gamma',\n",
       " 'exceptions',\n",
       " 'exp',\n",
       " 'exp2',\n",
       " 'expand_dims',\n",
       " 'expm1',\n",
       " 'expm1x',\n",
       " 'extract',\n",
       " 'eye',\n",
       " 'fabs',\n",
       " 'fastCopyAndTranspose',\n",
       " 'fft',\n",
       " 'fill_diagonal',\n",
       " 'find_common_type',\n",
       " 'finfo',\n",
       " 'fix',\n",
       " 'flatiter',\n",
       " 'flatnonzero',\n",
       " 'flexible',\n",
       " 'flip',\n",
       " 'fliplr',\n",
       " 'flipud',\n",
       " 'float16',\n",
       " 'float32',\n",
       " 'float64',\n",
       " 'float_',\n",
       " 'float_power',\n",
       " 'floating',\n",
       " 'floor',\n",
       " 'floor_divide',\n",
       " 'fmax',\n",
       " 'fmin',\n",
       " 'fmod',\n",
       " 'format_float_positional',\n",
       " 'format_float_scientific',\n",
       " 'format_parser',\n",
       " 'frexp',\n",
       " 'from_dlpack',\n",
       " 'frombuffer',\n",
       " 'fromfile',\n",
       " 'fromfunction',\n",
       " 'fromiter',\n",
       " 'frompyfunc',\n",
       " 'fromregex',\n",
       " 'fromstring',\n",
       " 'full',\n",
       " 'full_like',\n",
       " 'gcd',\n",
       " 'generic',\n",
       " 'genfromtxt',\n",
       " 'geomspace',\n",
       " 'get_array_wrap',\n",
       " 'get_include',\n",
       " 'get_printoptions',\n",
       " 'getbufsize',\n",
       " 'geterr',\n",
       " 'geterrcall',\n",
       " 'geterrobj',\n",
       " 'gradient',\n",
       " 'greater',\n",
       " 'greater_equal',\n",
       " 'half',\n",
       " 'hamming',\n",
       " 'hanning',\n",
       " 'heaviside',\n",
       " 'histogram',\n",
       " 'histogram2d',\n",
       " 'histogram_bin_edges',\n",
       " 'histogramdd',\n",
       " 'hsplit',\n",
       " 'hstack',\n",
       " 'hypot',\n",
       " 'i0',\n",
       " 'identity',\n",
       " 'iinfo',\n",
       " 'imag',\n",
       " 'in1d',\n",
       " 'index_exp',\n",
       " 'indices',\n",
       " 'inexact',\n",
       " 'inf',\n",
       " 'info',\n",
       " 'infty',\n",
       " 'inner',\n",
       " 'insert',\n",
       " 'int16',\n",
       " 'int32',\n",
       " 'int64',\n",
       " 'int8',\n",
       " 'int_',\n",
       " 'intc',\n",
       " 'integer',\n",
       " 'interp',\n",
       " 'intersect1d',\n",
       " 'intp',\n",
       " 'invert',\n",
       " 'is_busday',\n",
       " 'isclose',\n",
       " 'iscomplex',\n",
       " 'iscomplexobj',\n",
       " 'isfinite',\n",
       " 'isfortran',\n",
       " 'isin',\n",
       " 'isinf',\n",
       " 'isnan',\n",
       " 'isnat',\n",
       " 'isneginf',\n",
       " 'isposinf',\n",
       " 'isreal',\n",
       " 'isrealobj',\n",
       " 'isscalar',\n",
       " 'issctype',\n",
       " 'issubclass_',\n",
       " 'issubdtype',\n",
       " 'issubsctype',\n",
       " 'iterable',\n",
       " 'ix_',\n",
       " 'kaiser',\n",
       " 'kron',\n",
       " 'lcm',\n",
       " 'ldexp',\n",
       " 'left_shift',\n",
       " 'less',\n",
       " 'less_equal',\n",
       " 'lexsort',\n",
       " 'lib',\n",
       " 'linalg',\n",
       " 'linspace',\n",
       " 'little_endian',\n",
       " 'load',\n",
       " 'loadtxt',\n",
       " 'log',\n",
       " 'log10',\n",
       " 'log1p',\n",
       " 'log2',\n",
       " 'logaddexp',\n",
       " 'logaddexp2',\n",
       " 'logical_and',\n",
       " 'logical_not',\n",
       " 'logical_or',\n",
       " 'logical_xor',\n",
       " 'logspace',\n",
       " 'longcomplex',\n",
       " 'longdouble',\n",
       " 'longfloat',\n",
       " 'longlong',\n",
       " 'lookfor',\n",
       " 'ma',\n",
       " 'mask_indices',\n",
       " 'mat',\n",
       " 'matmul',\n",
       " 'matrix',\n",
       " 'max',\n",
       " 'maximum',\n",
       " 'maximum_sctype',\n",
       " 'may_share_memory',\n",
       " 'mean',\n",
       " 'median',\n",
       " 'memmap',\n",
       " 'meshgrid',\n",
       " 'mgrid',\n",
       " 'min',\n",
       " 'min_scalar_type',\n",
       " 'minimum',\n",
       " 'mintypecode',\n",
       " 'mod',\n",
       " 'modf',\n",
       " 'moveaxis',\n",
       " 'msort',\n",
       " 'multiply',\n",
       " 'nan',\n",
       " 'nan_to_num',\n",
       " 'nanargmax',\n",
       " 'nanargmin',\n",
       " 'nancumprod',\n",
       " 'nancumsum',\n",
       " 'nanmax',\n",
       " 'nanmean',\n",
       " 'nanmedian',\n",
       " 'nanmin',\n",
       " 'nanpercentile',\n",
       " 'nanprod',\n",
       " 'nanquantile',\n",
       " 'nanstd',\n",
       " 'nansum',\n",
       " 'nanvar',\n",
       " 'nbytes',\n",
       " 'ndarray',\n",
       " 'ndenumerate',\n",
       " 'ndim',\n",
       " 'ndindex',\n",
       " 'nditer',\n",
       " 'negative',\n",
       " 'nested_iters',\n",
       " 'newaxis',\n",
       " 'nextafter',\n",
       " 'nonzero',\n",
       " 'not_equal',\n",
       " 'numarray',\n",
       " 'number',\n",
       " 'obj2sctype',\n",
       " 'object_',\n",
       " 'ogrid',\n",
       " 'oldnumeric',\n",
       " 'ones',\n",
       " 'ones_like',\n",
       " 'outer',\n",
       " 'packbits',\n",
       " 'pad',\n",
       " 'partition',\n",
       " 'percentile',\n",
       " 'pi',\n",
       " 'piecewise',\n",
       " 'place',\n",
       " 'poly',\n",
       " 'poly1d',\n",
       " 'polyadd',\n",
       " 'polyder',\n",
       " 'polydiv',\n",
       " 'polyfit',\n",
       " 'polyint',\n",
       " 'polymul',\n",
       " 'polynomial',\n",
       " 'polysub',\n",
       " 'polyval',\n",
       " 'positive',\n",
       " 'power',\n",
       " 'printoptions',\n",
       " 'prod',\n",
       " 'product',\n",
       " 'promote_types',\n",
       " 'ptp',\n",
       " 'put',\n",
       " 'put_along_axis',\n",
       " 'putmask',\n",
       " 'quantile',\n",
       " 'r_',\n",
       " 'rad2deg',\n",
       " 'radians',\n",
       " 'random',\n",
       " 'ravel',\n",
       " 'ravel_multi_index',\n",
       " 'real',\n",
       " 'real_if_close',\n",
       " 'rec',\n",
       " 'recarray',\n",
       " 'recfromcsv',\n",
       " 'recfromtxt',\n",
       " 'reciprocal',\n",
       " 'record',\n",
       " 'remainder',\n",
       " 'repeat',\n",
       " 'require',\n",
       " 'reshape',\n",
       " 'resize',\n",
       " 'result_type',\n",
       " 'right_shift',\n",
       " 'rint',\n",
       " 'roll',\n",
       " 'rollaxis',\n",
       " 'roots',\n",
       " 'rot90',\n",
       " 'round',\n",
       " 'round_',\n",
       " 'row_stack',\n",
       " 's_',\n",
       " 'safe_eval',\n",
       " 'save',\n",
       " 'savetxt',\n",
       " 'savez',\n",
       " 'savez_compressed',\n",
       " 'sctype2char',\n",
       " 'sctypeDict',\n",
       " 'sctypes',\n",
       " 'searchsorted',\n",
       " 'select',\n",
       " 'set_numeric_ops',\n",
       " 'set_printoptions',\n",
       " 'set_string_function',\n",
       " 'setbufsize',\n",
       " 'setdiff1d',\n",
       " 'seterr',\n",
       " 'seterrcall',\n",
       " 'seterrobj',\n",
       " 'setxor1d',\n",
       " 'shape',\n",
       " 'shares_memory',\n",
       " 'short',\n",
       " 'show_config',\n",
       " 'show_runtime',\n",
       " 'sign',\n",
       " 'signbit',\n",
       " 'signedinteger',\n",
       " 'sin',\n",
       " 'sinc',\n",
       " 'single',\n",
       " 'singlecomplex',\n",
       " 'sinh',\n",
       " 'size',\n",
       " 'sometrue',\n",
       " 'sort',\n",
       " 'sort_complex',\n",
       " 'source',\n",
       " 'spacing',\n",
       " 'split',\n",
       " 'sqrt',\n",
       " 'square',\n",
       " 'squeeze',\n",
       " 'stack',\n",
       " 'std',\n",
       " 'str_',\n",
       " 'string_',\n",
       " 'subtract',\n",
       " 'sum',\n",
       " 'swapaxes',\n",
       " 'take',\n",
       " 'take_along_axis',\n",
       " 'tan',\n",
       " 'tanh',\n",
       " 'tensordot',\n",
       " 'test',\n",
       " 'testing',\n",
       " 'tile',\n",
       " 'timedelta64',\n",
       " 'trace',\n",
       " 'tracemalloc_domain',\n",
       " 'transpose',\n",
       " 'trapz',\n",
       " 'tri',\n",
       " 'tril',\n",
       " 'tril_indices',\n",
       " 'tril_indices_from',\n",
       " 'trim_zeros',\n",
       " 'triu',\n",
       " 'triu_indices',\n",
       " 'triu_indices_from',\n",
       " 'true_divide',\n",
       " 'trunc',\n",
       " 'typecodes',\n",
       " 'typename',\n",
       " 'typing',\n",
       " 'ubyte',\n",
       " 'ufunc',\n",
       " 'uint',\n",
       " 'uint16',\n",
       " 'uint32',\n",
       " 'uint64',\n",
       " 'uint8',\n",
       " 'uintc',\n",
       " 'uintp',\n",
       " 'ulonglong',\n",
       " 'unicode_',\n",
       " 'union1d',\n",
       " 'unique',\n",
       " 'unpackbits',\n",
       " 'unravel_index',\n",
       " 'unsignedinteger',\n",
       " 'unwrap',\n",
       " 'ushort',\n",
       " 'vander',\n",
       " 'var',\n",
       " 'vdot',\n",
       " 'vectorize',\n",
       " 'version',\n",
       " 'void',\n",
       " 'vsplit',\n",
       " 'vstack',\n",
       " 'where',\n",
       " 'who',\n",
       " 'zeros',\n",
       " 'zeros_like']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__all__\n",
    "np.__dict__\n",
    "np.__doc__\n",
    "np.__file__\n",
    "#np.__package__\n",
    "dir(np)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入股票模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入tushare\n",
    "import tushare as ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MailMerge',\n",
       " 'TraderAPI',\n",
       " '__author__',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " 'bar',\n",
       " 'bdi',\n",
       " 'broker_tops',\n",
       " 'cap_tops',\n",
       " 'close_apis',\n",
       " 'codecs',\n",
       " 'coins',\n",
       " 'coins_bar',\n",
       " 'coins_snapshot',\n",
       " 'coins_tick',\n",
       " 'coins_trade',\n",
       " 'day_boxoffice',\n",
       " 'day_cinema',\n",
       " 'forecast_data',\n",
       " 'fund',\n",
       " 'fund_holdings',\n",
       " 'futures',\n",
       " 'get_apis',\n",
       " 'get_area_classified',\n",
       " 'get_balance_sheet',\n",
       " 'get_cash_flow',\n",
       " 'get_cashflow_data',\n",
       " 'get_cffex_daily',\n",
       " 'get_concept_classified',\n",
       " 'get_cpi',\n",
       " 'get_czce_daily',\n",
       " 'get_day_all',\n",
       " 'get_dce_daily',\n",
       " 'get_debtpaying_data',\n",
       " 'get_deposit_rate',\n",
       " 'get_fund_info',\n",
       " 'get_future_daily',\n",
       " 'get_gdp_contrib',\n",
       " 'get_gdp_for',\n",
       " 'get_gdp_pull',\n",
       " 'get_gdp_quarter',\n",
       " 'get_gdp_year',\n",
       " 'get_gem_classified',\n",
       " 'get_gold_and_foreign_reserves',\n",
       " 'get_growth_data',\n",
       " 'get_h_data',\n",
       " 'get_hist_data',\n",
       " 'get_hists',\n",
       " 'get_hs300s',\n",
       " 'get_index',\n",
       " 'get_industry_classified',\n",
       " 'get_instrument',\n",
       " 'get_intlfuture',\n",
       " 'get_k_data',\n",
       " 'get_latest_news',\n",
       " 'get_loan_rate',\n",
       " 'get_markets',\n",
       " 'get_money_supply',\n",
       " 'get_money_supply_bal',\n",
       " 'get_nav_close',\n",
       " 'get_nav_grading',\n",
       " 'get_nav_history',\n",
       " 'get_nav_open',\n",
       " 'get_notices',\n",
       " 'get_operation_data',\n",
       " 'get_ppi',\n",
       " 'get_profit_data',\n",
       " 'get_profit_statement',\n",
       " 'get_realtime_quotes',\n",
       " 'get_report_data',\n",
       " 'get_rrr',\n",
       " 'get_shfe_daily',\n",
       " 'get_shfe_vwap',\n",
       " 'get_sina_dd',\n",
       " 'get_sme_classified',\n",
       " 'get_st_classified',\n",
       " 'get_stock_basics',\n",
       " 'get_suspended',\n",
       " 'get_sz50s',\n",
       " 'get_terminated',\n",
       " 'get_tick_data',\n",
       " 'get_today_all',\n",
       " 'get_today_ticks',\n",
       " 'get_token',\n",
       " 'get_zz500s',\n",
       " 'global_realtime',\n",
       " 'guba_sina',\n",
       " 'ht_subs',\n",
       " 'inst_detail',\n",
       " 'inst_tops',\n",
       " 'internet',\n",
       " 'is_holiday',\n",
       " 'latest_content',\n",
       " 'lpr_data',\n",
       " 'lpr_ma_data',\n",
       " 'margin_detail',\n",
       " 'margin_offset',\n",
       " 'margin_target',\n",
       " 'margin_zsl',\n",
       " 'moneyflow_hsgt',\n",
       " 'month_boxoffice',\n",
       " 'new_cbonds',\n",
       " 'new_stocks',\n",
       " 'notice_content',\n",
       " 'os',\n",
       " 'pledged_detail',\n",
       " 'pro',\n",
       " 'pro_api',\n",
       " 'pro_bar',\n",
       " 'profit_data',\n",
       " 'profit_divis',\n",
       " 'quotes',\n",
       " 'realtime_boxoffice',\n",
       " 'realtime_list',\n",
       " 'realtime_quote',\n",
       " 'realtime_tick',\n",
       " 'reset_instrument',\n",
       " 'set_token',\n",
       " 'sh_margin_details',\n",
       " 'sh_margins',\n",
       " 'shibor_data',\n",
       " 'shibor_ma_data',\n",
       " 'shibor_quote_data',\n",
       " 'stock',\n",
       " 'stock_issuance',\n",
       " 'stock_pledged',\n",
       " 'subs',\n",
       " 'sz_margin_details',\n",
       " 'sz_margins',\n",
       " 'tick',\n",
       " 'top10_holders',\n",
       " 'top_list',\n",
       " 'trade_cal',\n",
       " 'trader',\n",
       " 'util',\n",
       " 'xsg_data']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(ts)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tushare 初始化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tushare as ts\n",
    "\n",
    "# 设置 token 并初始化\n",
    "ts.set_token('ca0f845f997ade304d2b8fa2d4f63d3a1ba9e0e5863bba343d94ae52')\n",
    "pro = ts.pro_api()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function pro_api in module tushare.pro.data_pro:\n",
      "\n",
      "pro_api(token='', timeout=30)\n",
      "    初始化pro API,第一次可以通过ts.set_token('your token')来记录自己的token凭证，临时token可以通过本参数传入\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#pro=ts.get_hist_data('603722')\n",
    "help(ts.pro_api)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A股日线行情\n",
    "\n",
    "接口：daily，可以通过数据工具调试和查看数据   \n",
    "数据说明：交易日每天15点～16点之间入库。本接口是未复权行情，停牌期间不提供数据   \n",
    "调取说明：120积分每分钟内最多调取500次，每次6000条数据，相当于单次提取23年历史   \n",
    "描述：获取股票行情数据，或通过通用行情接口获取数据，包含了前后复权数据   \n",
    "\n",
    "<p><strong>输入参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>必选</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>股票代码（支持多个股票同时提取，逗号分隔）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>交易日期（YYYYMMDD）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>start_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>开始日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>end_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>结束日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "</tbody>\n",
    "</table>\n",
    "\n",
    "\n",
    "<p><strong>注：日期都填YYYYMMDD格式，比如20181010</strong></p>\n",
    "<p><strong>输出参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>股票代码</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>交易日期</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>open</td>\n",
    "<td>float</td>\n",
    "<td>开盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>high</td>\n",
    "<td>float</td>\n",
    "<td>最高价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>low</td>\n",
    "<td>float</td>\n",
    "<td>最低价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>close</td>\n",
    "<td>float</td>\n",
    "<td>收盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pre_close</td>\n",
    "<td>float</td>\n",
    "<td>昨收价(前复权)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>change</td>\n",
    "<td>float</td>\n",
    "<td>涨跌额</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pct_chg</td>\n",
    "<td>float</td>\n",
    "<td>涨跌幅 （未复权，如果是复权请用 <a href=\"https://tushare.pro/document/2?doc_id=109\">通用行情接口</a> ）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>vol</td>\n",
    "<td>float</td>\n",
    "<td>成交量 （手）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>amount</td>\n",
    "<td>float</td>\n",
    "<td>成交额 （千元）</td>\n",
    "</tr>\n",
    "</tbody></table>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### 初始化pro接口\n",
    "### 在这个网址https://tushare.pro/注册，并在此处获得token，https://tushare.pro/user/token\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
      "0     000011.SZ   20241225   8.92   9.01   8.70   8.91       8.92   -0.01   \n",
      "1     000011.SZ   20241224   9.00   9.00   8.87   8.92       8.94   -0.02   \n",
      "2     000011.SZ   20241223   9.32   9.32   8.90   8.94       9.34   -0.40   \n",
      "3     000011.SZ   20241220   9.38   9.52   9.29   9.34       9.43   -0.09   \n",
      "4     000011.SZ   20241219   9.31   9.49   9.26   9.43       9.41    0.02   \n",
      "...         ...        ...    ...    ...    ...    ...        ...     ...   \n",
      "3607  000011.SZ   20100108  10.01  10.20  10.01  10.20      10.10    0.10   \n",
      "3608  000011.SZ   20100107  10.18  10.41  10.01  10.10      10.34   -0.24   \n",
      "3609  000011.SZ   20100106  10.27  10.52  10.11  10.34      10.31    0.03   \n",
      "3610  000011.SZ   20100105  10.60  10.71  10.21  10.31      10.64   -0.33   \n",
      "3611  000011.SZ   20100104  10.79  10.79  10.63  10.64      10.69   -0.05   \n",
      "\n",
      "      pct_chg       vol      amount  \n",
      "0     -0.1121  52061.40  46013.4690  \n",
      "1     -0.2237  40776.00  36435.6340  \n",
      "2     -4.2827  75677.74  68507.9980  \n",
      "3     -0.9544  62003.99  58206.3700  \n",
      "4      0.2125  48930.00  45983.1970  \n",
      "...       ...       ...         ...  \n",
      "3607   0.9900  37953.57  38441.9952  \n",
      "3608  -2.3200  53449.53  54344.5726  \n",
      "3609   0.2900  61389.02  63360.6015  \n",
      "3610  -3.1000  68653.89  71046.6895  \n",
      "3611  -0.4700  36259.40  38667.4625  \n",
      "\n",
      "[3612 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "pro = ts.pro_api('ca0f845f997ade304d2b8fa2d4f63d3a1ba9e0e5863bba343d94ae52')\n",
    "# 拉取数据\n",
    "df = pro.daily(**{\n",
    "    \"ts_code\": \"000011.SZ\",\n",
    "    \"trade_date\": \"\",\n",
    "    \"start_date\": 20100101,\n",
    "    \"end_date\": 20241225,\n",
    "    \"offset\": \"\",\n",
    "    \"limit\": \"\"\n",
    "}, fields=[\n",
    "    \"ts_code\",\n",
    "    \"trade_date\",\n",
    "    \"open\",\n",
    "    \"high\",\n",
    "    \"low\",\n",
    "    \"close\",\n",
    "    \"pre_close\",\n",
    "    \"change\",\n",
    "    \"pct_chg\",\n",
    "    \"vol\",\n",
    "    \"amount\"\n",
    "])\n",
    "print(df)\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
      "0     000011.SZ   20241225   8.92   9.01   8.70   8.91       8.92   -0.01   \n",
      "1     000011.SZ   20241224   9.00   9.00   8.87   8.92       8.94   -0.02   \n",
      "2     000011.SZ   20241223   9.32   9.32   8.90   8.94       9.34   -0.40   \n",
      "3     000011.SZ   20241220   9.38   9.52   9.29   9.34       9.43   -0.09   \n",
      "4     000011.SZ   20241219   9.31   9.49   9.26   9.43       9.41    0.02   \n",
      "...         ...        ...    ...    ...    ...    ...        ...     ...   \n",
      "3607  000011.SZ   20100108  10.01  10.20  10.01  10.20      10.10    0.10   \n",
      "3608  000011.SZ   20100107  10.18  10.41  10.01  10.10      10.34   -0.24   \n",
      "3609  000011.SZ   20100106  10.27  10.52  10.11  10.34      10.31    0.03   \n",
      "3610  000011.SZ   20100105  10.60  10.71  10.21  10.31      10.64   -0.33   \n",
      "3611  000011.SZ   20100104  10.79  10.79  10.63  10.64      10.69   -0.05   \n",
      "\n",
      "      pct_chg       vol      amount  \n",
      "0     -0.1121  52061.40  46013.4690  \n",
      "1     -0.2237  40776.00  36435.6340  \n",
      "2     -4.2827  75677.74  68507.9980  \n",
      "3     -0.9544  62003.99  58206.3700  \n",
      "4      0.2125  48930.00  45983.1970  \n",
      "...       ...       ...         ...  \n",
      "3607   0.9900  37953.57  38441.9952  \n",
      "3608  -2.3200  53449.53  54344.5726  \n",
      "3609   0.2900  61389.02  63360.6015  \n",
      "3610  -3.1000  68653.89  71046.6895  \n",
      "3611  -0.4700  36259.40  38667.4625  \n",
      "\n",
      "[3612 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "### 存储数据\n",
    "print(df)\n",
    "df.to_csv(\"000011.csv\")\n",
    "stockname='\t深物业A'\n",
    "stockcode='000011'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>3602</th>\n",
       "      <th>3603</th>\n",
       "      <th>3604</th>\n",
       "      <th>3605</th>\n",
       "      <th>3606</th>\n",
       "      <th>3607</th>\n",
       "      <th>3608</th>\n",
       "      <th>3609</th>\n",
       "      <th>3610</th>\n",
       "      <th>3611</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ts_code</th>\n",
       "      <td>000011.SZ</td>\n",
       "      <td>000011.SZ</td>\n",
       "      <td>000011.SZ</td>\n",
       "      <td>000011.SZ</td>\n",
       "      <td>000011.SZ</td>\n",
       "      <td>000011.SZ</td>\n",
       "      <td>000011.SZ</td>\n",
       "      <td>000011.SZ</td>\n",
       "      <td>000011.SZ</td>\n",
       "      <td>000011.SZ</td>\n",
       "      <td>...</td>\n",
       "      <td>000011.SZ</td>\n",
       "      <td>000011.SZ</td>\n",
       "      <td>000011.SZ</td>\n",
       "      <td>000011.SZ</td>\n",
       "      <td>000011.SZ</td>\n",
       "      <td>000011.SZ</td>\n",
       "      <td>000011.SZ</td>\n",
       "      <td>000011.SZ</td>\n",
       "      <td>000011.SZ</td>\n",
       "      <td>000011.SZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <td>20241225</td>\n",
       "      <td>20241224</td>\n",
       "      <td>20241223</td>\n",
       "      <td>20241220</td>\n",
       "      <td>20241219</td>\n",
       "      <td>20241218</td>\n",
       "      <td>20241217</td>\n",
       "      <td>20241216</td>\n",
       "      <td>20241213</td>\n",
       "      <td>20241212</td>\n",
       "      <td>...</td>\n",
       "      <td>20100115</td>\n",
       "      <td>20100114</td>\n",
       "      <td>20100113</td>\n",
       "      <td>20100112</td>\n",
       "      <td>20100111</td>\n",
       "      <td>20100108</td>\n",
       "      <td>20100107</td>\n",
       "      <td>20100106</td>\n",
       "      <td>20100105</td>\n",
       "      <td>20100104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open</th>\n",
       "      <td>8.92</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.32</td>\n",
       "      <td>9.38</td>\n",
       "      <td>9.31</td>\n",
       "      <td>9.31</td>\n",
       "      <td>9.65</td>\n",
       "      <td>9.73</td>\n",
       "      <td>9.94</td>\n",
       "      <td>9.83</td>\n",
       "      <td>...</td>\n",
       "      <td>10.3</td>\n",
       "      <td>10.31</td>\n",
       "      <td>10.5</td>\n",
       "      <td>10.4</td>\n",
       "      <td>10.25</td>\n",
       "      <td>10.01</td>\n",
       "      <td>10.18</td>\n",
       "      <td>10.27</td>\n",
       "      <td>10.6</td>\n",
       "      <td>10.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>9.01</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.32</td>\n",
       "      <td>9.52</td>\n",
       "      <td>9.49</td>\n",
       "      <td>9.55</td>\n",
       "      <td>9.75</td>\n",
       "      <td>9.8</td>\n",
       "      <td>9.96</td>\n",
       "      <td>10.1</td>\n",
       "      <td>...</td>\n",
       "      <td>10.77</td>\n",
       "      <td>10.46</td>\n",
       "      <td>10.52</td>\n",
       "      <td>10.84</td>\n",
       "      <td>10.52</td>\n",
       "      <td>10.2</td>\n",
       "      <td>10.41</td>\n",
       "      <td>10.52</td>\n",
       "      <td>10.71</td>\n",
       "      <td>10.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>8.7</td>\n",
       "      <td>8.87</td>\n",
       "      <td>8.9</td>\n",
       "      <td>9.29</td>\n",
       "      <td>9.26</td>\n",
       "      <td>9.25</td>\n",
       "      <td>9.28</td>\n",
       "      <td>9.59</td>\n",
       "      <td>9.72</td>\n",
       "      <td>9.74</td>\n",
       "      <td>...</td>\n",
       "      <td>10.3</td>\n",
       "      <td>10.12</td>\n",
       "      <td>10.12</td>\n",
       "      <td>10.28</td>\n",
       "      <td>10.04</td>\n",
       "      <td>10.01</td>\n",
       "      <td>10.01</td>\n",
       "      <td>10.11</td>\n",
       "      <td>10.21</td>\n",
       "      <td>10.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>close</th>\n",
       "      <td>8.91</td>\n",
       "      <td>8.92</td>\n",
       "      <td>8.94</td>\n",
       "      <td>9.34</td>\n",
       "      <td>9.43</td>\n",
       "      <td>9.41</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.66</td>\n",
       "      <td>9.74</td>\n",
       "      <td>10.02</td>\n",
       "      <td>...</td>\n",
       "      <td>10.58</td>\n",
       "      <td>10.28</td>\n",
       "      <td>10.15</td>\n",
       "      <td>10.71</td>\n",
       "      <td>10.47</td>\n",
       "      <td>10.2</td>\n",
       "      <td>10.1</td>\n",
       "      <td>10.34</td>\n",
       "      <td>10.31</td>\n",
       "      <td>10.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pre_close</th>\n",
       "      <td>8.92</td>\n",
       "      <td>8.94</td>\n",
       "      <td>9.34</td>\n",
       "      <td>9.43</td>\n",
       "      <td>9.41</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.66</td>\n",
       "      <td>9.74</td>\n",
       "      <td>10.02</td>\n",
       "      <td>9.85</td>\n",
       "      <td>...</td>\n",
       "      <td>10.28</td>\n",
       "      <td>10.15</td>\n",
       "      <td>10.71</td>\n",
       "      <td>10.47</td>\n",
       "      <td>10.2</td>\n",
       "      <td>10.1</td>\n",
       "      <td>10.34</td>\n",
       "      <td>10.31</td>\n",
       "      <td>10.64</td>\n",
       "      <td>10.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>change</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>0.17</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_chg</th>\n",
       "      <td>-0.1121</td>\n",
       "      <td>-0.2237</td>\n",
       "      <td>-4.2827</td>\n",
       "      <td>-0.9544</td>\n",
       "      <td>0.2125</td>\n",
       "      <td>1.1828</td>\n",
       "      <td>-3.7267</td>\n",
       "      <td>-0.8214</td>\n",
       "      <td>-2.7944</td>\n",
       "      <td>1.7259</td>\n",
       "      <td>...</td>\n",
       "      <td>2.92</td>\n",
       "      <td>1.28</td>\n",
       "      <td>-5.23</td>\n",
       "      <td>2.29</td>\n",
       "      <td>2.65</td>\n",
       "      <td>0.99</td>\n",
       "      <td>-2.32</td>\n",
       "      <td>0.29</td>\n",
       "      <td>-3.1</td>\n",
       "      <td>-0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol</th>\n",
       "      <td>52061.4</td>\n",
       "      <td>40776.0</td>\n",
       "      <td>75677.74</td>\n",
       "      <td>62003.99</td>\n",
       "      <td>48930.0</td>\n",
       "      <td>54644.78</td>\n",
       "      <td>81529.0</td>\n",
       "      <td>70269.74</td>\n",
       "      <td>95444.0</td>\n",
       "      <td>135611.11</td>\n",
       "      <td>...</td>\n",
       "      <td>85852.53</td>\n",
       "      <td>50884.59</td>\n",
       "      <td>64925.59</td>\n",
       "      <td>95396.13</td>\n",
       "      <td>73829.46</td>\n",
       "      <td>37953.57</td>\n",
       "      <td>53449.53</td>\n",
       "      <td>61389.02</td>\n",
       "      <td>68653.89</td>\n",
       "      <td>36259.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount</th>\n",
       "      <td>46013.469</td>\n",
       "      <td>36435.634</td>\n",
       "      <td>68507.998</td>\n",
       "      <td>58206.37</td>\n",
       "      <td>45983.197</td>\n",
       "      <td>51320.285</td>\n",
       "      <td>76896.477</td>\n",
       "      <td>68124.653</td>\n",
       "      <td>93535.407</td>\n",
       "      <td>135163.115</td>\n",
       "      <td>...</td>\n",
       "      <td>90770.826</td>\n",
       "      <td>52272.615</td>\n",
       "      <td>66945.9861</td>\n",
       "      <td>101360.2278</td>\n",
       "      <td>75789.4584</td>\n",
       "      <td>38441.9952</td>\n",
       "      <td>54344.5726</td>\n",
       "      <td>63360.6015</td>\n",
       "      <td>71046.6895</td>\n",
       "      <td>38667.4625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 3612 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0          1          2          3          4          5     \\\n",
       "ts_code     000011.SZ  000011.SZ  000011.SZ  000011.SZ  000011.SZ  000011.SZ   \n",
       "trade_date   20241225   20241224   20241223   20241220   20241219   20241218   \n",
       "open             8.92        9.0       9.32       9.38       9.31       9.31   \n",
       "high             9.01        9.0       9.32       9.52       9.49       9.55   \n",
       "low               8.7       8.87        8.9       9.29       9.26       9.25   \n",
       "close            8.91       8.92       8.94       9.34       9.43       9.41   \n",
       "pre_close        8.92       8.94       9.34       9.43       9.41        9.3   \n",
       "change          -0.01      -0.02       -0.4      -0.09       0.02       0.11   \n",
       "pct_chg       -0.1121    -0.2237    -4.2827    -0.9544     0.2125     1.1828   \n",
       "vol           52061.4    40776.0   75677.74   62003.99    48930.0   54644.78   \n",
       "amount      46013.469  36435.634  68507.998   58206.37  45983.197  51320.285   \n",
       "\n",
       "                 6          7          8           9     ...       3602  \\\n",
       "ts_code     000011.SZ  000011.SZ  000011.SZ   000011.SZ  ...  000011.SZ   \n",
       "trade_date   20241217   20241216   20241213    20241212  ...   20100115   \n",
       "open             9.65       9.73       9.94        9.83  ...       10.3   \n",
       "high             9.75        9.8       9.96        10.1  ...      10.77   \n",
       "low              9.28       9.59       9.72        9.74  ...       10.3   \n",
       "close             9.3       9.66       9.74       10.02  ...      10.58   \n",
       "pre_close        9.66       9.74      10.02        9.85  ...      10.28   \n",
       "change          -0.36      -0.08      -0.28        0.17  ...        0.3   \n",
       "pct_chg       -3.7267    -0.8214    -2.7944      1.7259  ...       2.92   \n",
       "vol           81529.0   70269.74    95444.0   135611.11  ...   85852.53   \n",
       "amount      76896.477  68124.653  93535.407  135163.115  ...  90770.826   \n",
       "\n",
       "                 3603        3604         3605        3606        3607  \\\n",
       "ts_code     000011.SZ   000011.SZ    000011.SZ   000011.SZ   000011.SZ   \n",
       "trade_date   20100114    20100113     20100112    20100111    20100108   \n",
       "open            10.31        10.5         10.4       10.25       10.01   \n",
       "high            10.46       10.52        10.84       10.52        10.2   \n",
       "low             10.12       10.12        10.28       10.04       10.01   \n",
       "close           10.28       10.15        10.71       10.47        10.2   \n",
       "pre_close       10.15       10.71        10.47        10.2        10.1   \n",
       "change           0.13       -0.56         0.24        0.27         0.1   \n",
       "pct_chg          1.28       -5.23         2.29        2.65        0.99   \n",
       "vol          50884.59    64925.59     95396.13    73829.46    37953.57   \n",
       "amount      52272.615  66945.9861  101360.2278  75789.4584  38441.9952   \n",
       "\n",
       "                  3608        3609        3610        3611  \n",
       "ts_code      000011.SZ   000011.SZ   000011.SZ   000011.SZ  \n",
       "trade_date    20100107    20100106    20100105    20100104  \n",
       "open             10.18       10.27        10.6       10.79  \n",
       "high             10.41       10.52       10.71       10.79  \n",
       "low              10.01       10.11       10.21       10.63  \n",
       "close             10.1       10.34       10.31       10.64  \n",
       "pre_close        10.34       10.31       10.64       10.69  \n",
       "change           -0.24        0.03       -0.33       -0.05  \n",
       "pct_chg          -2.32        0.29        -3.1       -0.47  \n",
       "vol           53449.53    61389.02    68653.89     36259.4  \n",
       "amount      54344.5726  63360.6015  71046.6895  38667.4625  \n",
       "\n",
       "[11 rows x 3612 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据转置\n",
    "df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#输出图形\n",
    "df[\"close\"].plot(figsize=(16,4),legend=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b8629316a0>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#读取数据\n",
    "#coding=utf-8\n",
    "#使用pandas读取数据\n",
    "mydata=pd.read_csv(\"./000011.csv\",parse_dates=[\"trade_date\"],index_col=\"trade_date\")[[\"open\",\"high\",\"low\",\"close\"]]\n",
    "\n",
    "# data=pd.read_csv(\"./stock688333.csv\",parse_dates=[\"trade_date\"])[[\"trade_date\",\"open\",\"high\",\"low\",\"close\",\"vol\"]]\n",
    "\n",
    "#翻转数据\n",
    "\n",
    "mydata=mydata.iloc[::-1]\n",
    "\n",
    "plt.plot(mydata[\"close\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3582 entries, 0 to 3581\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  3582 non-null   datetime64[ns]\n",
      " 1   open        3582 non-null   float64       \n",
      " 2   high        3582 non-null   float64       \n",
      " 3   low         3582 non-null   float64       \n",
      " 4   close       3582 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 140.1 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3577</th>\n",
       "      <td>2024-12-19</td>\n",
       "      <td>9.31</td>\n",
       "      <td>9.49</td>\n",
       "      <td>9.26</td>\n",
       "      <td>9.43</td>\n",
       "      <td>9.508</td>\n",
       "      <td>9.658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3578</th>\n",
       "      <td>2024-12-20</td>\n",
       "      <td>9.38</td>\n",
       "      <td>9.52</td>\n",
       "      <td>9.29</td>\n",
       "      <td>9.34</td>\n",
       "      <td>9.428</td>\n",
       "      <td>9.606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3579</th>\n",
       "      <td>2024-12-23</td>\n",
       "      <td>9.32</td>\n",
       "      <td>9.32</td>\n",
       "      <td>8.90</td>\n",
       "      <td>8.94</td>\n",
       "      <td>9.284</td>\n",
       "      <td>9.548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3580</th>\n",
       "      <td>2024-12-24</td>\n",
       "      <td>9.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>8.87</td>\n",
       "      <td>8.92</td>\n",
       "      <td>9.208</td>\n",
       "      <td>9.461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3581</th>\n",
       "      <td>2024-12-25</td>\n",
       "      <td>8.92</td>\n",
       "      <td>9.01</td>\n",
       "      <td>8.70</td>\n",
       "      <td>8.91</td>\n",
       "      <td>9.108</td>\n",
       "      <td>9.367</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade_date  open  high   low  close      5     10\n",
       "3577 2024-12-19  9.31  9.49  9.26   9.43  9.508  9.658\n",
       "3578 2024-12-20  9.38  9.52  9.29   9.34  9.428  9.606\n",
       "3579 2024-12-23  9.32  9.32  8.90   8.94  9.284  9.548\n",
       "3580 2024-12-24  9.00  9.00  8.87   8.92  9.208  9.461\n",
       "3581 2024-12-25  8.92  9.01  8.70   8.91  9.108  9.367"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "# import akshare as ak\n",
    "\n",
    "df3 = mydata.reset_index().iloc[30:, :6]  # 取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 计算均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "\n",
    "df3.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "\n",
    "import mplfinance as mpf\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\python\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 9 (\t) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(stockname, fontsize=8)\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "plt.xticks(ticks =  np.arange(0,len(df3)), labels = df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy() )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from matplotlib.dates  import date2num\n",
    "#data['date']=date2num(data.index.to_pydatetime())\n",
    "#data=data.sort_values(by=\"date\",ascending=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     trade_date   open   high    low  close\n",
      "0    2010-01-04  10.79  10.79  10.63  10.64\n",
      "1    2010-01-05  10.60  10.71  10.21  10.31\n",
      "2    2010-01-06  10.27  10.52  10.11  10.34\n",
      "3    2010-01-07  10.18  10.41  10.01  10.10\n",
      "4    2010-01-08  10.01  10.20  10.01  10.20\n",
      "...         ...    ...    ...    ...    ...\n",
      "3607 2024-12-19   9.31   9.49   9.26   9.43\n",
      "3608 2024-12-20   9.38   9.52   9.29   9.34\n",
      "3609 2024-12-23   9.32   9.32   8.90   8.94\n",
      "3610 2024-12-24   9.00   9.00   8.87   8.92\n",
      "3611 2024-12-25   8.92   9.01   8.70   8.91\n",
      "\n",
      "[3612 rows x 5 columns]\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3612 entries, 0 to 3611\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  3612 non-null   datetime64[ns]\n",
      " 1   open        3612 non-null   float64       \n",
      " 2   high        3612 non-null   float64       \n",
      " 3   low         3612 non-null   float64       \n",
      " 4   close       3612 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 141.2 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "      <th>30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>10.79</td>\n",
       "      <td>10.79</td>\n",
       "      <td>10.63</td>\n",
       "      <td>10.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>10.60</td>\n",
       "      <td>10.71</td>\n",
       "      <td>10.21</td>\n",
       "      <td>10.31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>10.27</td>\n",
       "      <td>10.52</td>\n",
       "      <td>10.11</td>\n",
       "      <td>10.34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-07</td>\n",
       "      <td>10.18</td>\n",
       "      <td>10.41</td>\n",
       "      <td>10.01</td>\n",
       "      <td>10.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-08</td>\n",
       "      <td>10.01</td>\n",
       "      <td>10.20</td>\n",
       "      <td>10.01</td>\n",
       "      <td>10.20</td>\n",
       "      <td>10.318</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3607</th>\n",
       "      <td>2024-12-19</td>\n",
       "      <td>9.31</td>\n",
       "      <td>9.49</td>\n",
       "      <td>9.26</td>\n",
       "      <td>9.43</td>\n",
       "      <td>9.508</td>\n",
       "      <td>9.658</td>\n",
       "      <td>9.697667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3608</th>\n",
       "      <td>2024-12-20</td>\n",
       "      <td>9.38</td>\n",
       "      <td>9.52</td>\n",
       "      <td>9.29</td>\n",
       "      <td>9.34</td>\n",
       "      <td>9.428</td>\n",
       "      <td>9.606</td>\n",
       "      <td>9.675667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3609</th>\n",
       "      <td>2024-12-23</td>\n",
       "      <td>9.32</td>\n",
       "      <td>9.32</td>\n",
       "      <td>8.90</td>\n",
       "      <td>8.94</td>\n",
       "      <td>9.284</td>\n",
       "      <td>9.548</td>\n",
       "      <td>9.641333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3610</th>\n",
       "      <td>2024-12-24</td>\n",
       "      <td>9.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>8.87</td>\n",
       "      <td>8.92</td>\n",
       "      <td>9.208</td>\n",
       "      <td>9.461</td>\n",
       "      <td>9.588000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3611</th>\n",
       "      <td>2024-12-25</td>\n",
       "      <td>8.92</td>\n",
       "      <td>9.01</td>\n",
       "      <td>8.70</td>\n",
       "      <td>8.91</td>\n",
       "      <td>9.108</td>\n",
       "      <td>9.367</td>\n",
       "      <td>9.538667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3612 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade_date   open   high    low  close       5     10        30\n",
       "0    2010-01-04  10.79  10.79  10.63  10.64     NaN    NaN       NaN\n",
       "1    2010-01-05  10.60  10.71  10.21  10.31     NaN    NaN       NaN\n",
       "2    2010-01-06  10.27  10.52  10.11  10.34     NaN    NaN       NaN\n",
       "3    2010-01-07  10.18  10.41  10.01  10.10     NaN    NaN       NaN\n",
       "4    2010-01-08  10.01  10.20  10.01  10.20  10.318    NaN       NaN\n",
       "...         ...    ...    ...    ...    ...     ...    ...       ...\n",
       "3607 2024-12-19   9.31   9.49   9.26   9.43   9.508  9.658  9.697667\n",
       "3608 2024-12-20   9.38   9.52   9.29   9.34   9.428  9.606  9.675667\n",
       "3609 2024-12-23   9.32   9.32   8.90   8.94   9.284  9.548  9.641333\n",
       "3610 2024-12-24   9.00   9.00   8.87   8.92   9.208  9.461  9.588000\n",
       "3611 2024-12-25   8.92   9.01   8.70   8.91   9.108  9.367  9.538667\n",
       "\n",
       "[3612 rows x 8 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "df3 = mydata.reset_index().iloc[:,:]  #取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "print(df3)\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "df3['30']=df3.close.rolling(30).mean()\n",
    "\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\python\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 9 (\t) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(stockname+\"股价走势\", fontsize=8)\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "plt.plot(df3['30'].values,alpha=0.5, label='MA30')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "tempXticks=np.arange(0,len(df3))\n",
    "#XticksData=data.asfreq(\"2M\").dropna()\n",
    "nameXticks =  df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy()\n",
    "\n",
    "plt.xticks(ticks =tempXticks , labels =nameXticks )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "#修改X轴间隔\n",
    "x_major_locator=plt.MultipleLocator(10)\n",
    "ax.xaxis.set_major_locator(x_major_locator)\n",
    "ax.spines['bottom'].set_color('red')\n",
    "ax.spines['left'].set_color('red')\n",
    "plt.xlim(0,100)\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data1=pd.DataFrame(data,columns=[\"trade_date\",\"close\"])\n",
    "#data1=pd.DataFrame(data,columns=[\"close\"])\n",
    "data1=mydata\n",
    "data1[\"open\"].plot(label=\"open\")\n",
    "data1[\"close\"].plot(label=\"close\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.items of       trade_date  close\n",
       "0           8.92   9.01\n",
       "1           9.00   9.00\n",
       "2           9.32   9.32\n",
       "3           9.38   9.52\n",
       "4           9.31   9.49\n",
       "...          ...    ...\n",
       "3607       10.01  10.20\n",
       "3608       10.18  10.41\n",
       "3609       10.27  10.52\n",
       "3610       10.60  10.71\n",
       "3611       10.79  10.79\n",
       "\n",
       "[3612 rows x 2 columns]>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddd=[]\n",
    "for item in data1.items():\n",
    "    ddd.append(item)\n",
    "ind=ddd[0][1].values\n",
    "da1=ddd[1][1].values\n",
    "index1=[]\n",
    "data2=[]\n",
    "for item in ind:\n",
    "    index1.append(item)\n",
    "index1.reverse()\n",
    "for item in da1:\n",
    "    data2.append(item)\n",
    "data2.reverse()\n",
    "data1={'trade_date':index1,'close':data2}\n",
    "stockdata=pd.DataFrame(data1)\n",
    "\n",
    "stockdata.items    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10.64],\n",
       "       [10.31],\n",
       "       [10.34],\n",
       "       ...,\n",
       "       [ 8.94],\n",
       "       [ 8.92],\n",
       "       [ 8.91]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据检查\n",
    "df3.iloc[:,4:5].values[:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.dates  import date2num\n",
    "date2num(df3.trade_date.values)\n",
    "#添加data数据列\n",
    "df3['date']=date2num(df3.trade_date.values)\n",
    "#df3=df3.sort_values(by=\"date\",ascending=True)\n",
    "#df3=df3[['date','open', 'high', 'low', 'close']]\n",
    "\n",
    "ind=np.arange(0,2677)\n",
    "dataf1=df3[['date','open', 'high', 'low', 'close']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14613.0</th>\n",
       "      <td>10.79</td>\n",
       "      <td>10.79</td>\n",
       "      <td>10.63</td>\n",
       "      <td>10.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14614.0</th>\n",
       "      <td>10.60</td>\n",
       "      <td>10.71</td>\n",
       "      <td>10.21</td>\n",
       "      <td>10.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14615.0</th>\n",
       "      <td>10.27</td>\n",
       "      <td>10.52</td>\n",
       "      <td>10.11</td>\n",
       "      <td>10.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14616.0</th>\n",
       "      <td>10.18</td>\n",
       "      <td>10.41</td>\n",
       "      <td>10.01</td>\n",
       "      <td>10.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14617.0</th>\n",
       "      <td>10.01</td>\n",
       "      <td>10.20</td>\n",
       "      <td>10.01</td>\n",
       "      <td>10.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20076.0</th>\n",
       "      <td>9.31</td>\n",
       "      <td>9.49</td>\n",
       "      <td>9.26</td>\n",
       "      <td>9.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20077.0</th>\n",
       "      <td>9.38</td>\n",
       "      <td>9.52</td>\n",
       "      <td>9.29</td>\n",
       "      <td>9.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20080.0</th>\n",
       "      <td>9.32</td>\n",
       "      <td>9.32</td>\n",
       "      <td>8.90</td>\n",
       "      <td>8.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20081.0</th>\n",
       "      <td>9.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>8.87</td>\n",
       "      <td>8.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20082.0</th>\n",
       "      <td>8.92</td>\n",
       "      <td>9.01</td>\n",
       "      <td>8.70</td>\n",
       "      <td>8.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3612 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          open   high    low  close\n",
       "date                               \n",
       "14613.0  10.79  10.79  10.63  10.64\n",
       "14614.0  10.60  10.71  10.21  10.31\n",
       "14615.0  10.27  10.52  10.11  10.34\n",
       "14616.0  10.18  10.41  10.01  10.10\n",
       "14617.0  10.01  10.20  10.01  10.20\n",
       "...        ...    ...    ...    ...\n",
       "20076.0   9.31   9.49   9.26   9.43\n",
       "20077.0   9.38   9.52   9.29   9.34\n",
       "20080.0   9.32   9.32   8.90   8.94\n",
       "20081.0   9.00   9.00   8.87   8.92\n",
       "20082.0   8.92   9.01   8.70   8.91\n",
       "\n",
       "[3612 rows x 4 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf1.set_index(\"date\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 深度学习拟合股票"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 确保结果尽可能重现\n",
    "\n",
    "seed(1)\n",
    "tf.random.set_seed(1)\n",
    "\n",
    "# 设置相关参数\n",
    "n_timestamp  = 5    # 时间戳\n",
    "n_epochs     = 30    # 训练轮数\n",
    "# ====================================\n",
    "#      选择模型：\n",
    "#            1: 单层 LSTM\n",
    "#            2: 多层 LSTM\n",
    "#            3: 双向 LSTM\n",
    "# ====================================\n",
    "model_type = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14613.0</td>\n",
       "      <td>10.79</td>\n",
       "      <td>10.79</td>\n",
       "      <td>10.63</td>\n",
       "      <td>10.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14614.0</td>\n",
       "      <td>10.60</td>\n",
       "      <td>10.71</td>\n",
       "      <td>10.21</td>\n",
       "      <td>10.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14615.0</td>\n",
       "      <td>10.27</td>\n",
       "      <td>10.52</td>\n",
       "      <td>10.11</td>\n",
       "      <td>10.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14616.0</td>\n",
       "      <td>10.18</td>\n",
       "      <td>10.41</td>\n",
       "      <td>10.01</td>\n",
       "      <td>10.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14617.0</td>\n",
       "      <td>10.01</td>\n",
       "      <td>10.20</td>\n",
       "      <td>10.01</td>\n",
       "      <td>10.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3607</th>\n",
       "      <td>20076.0</td>\n",
       "      <td>9.31</td>\n",
       "      <td>9.49</td>\n",
       "      <td>9.26</td>\n",
       "      <td>9.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3608</th>\n",
       "      <td>20077.0</td>\n",
       "      <td>9.38</td>\n",
       "      <td>9.52</td>\n",
       "      <td>9.29</td>\n",
       "      <td>9.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3609</th>\n",
       "      <td>20080.0</td>\n",
       "      <td>9.32</td>\n",
       "      <td>9.32</td>\n",
       "      <td>8.90</td>\n",
       "      <td>8.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3610</th>\n",
       "      <td>20081.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>8.87</td>\n",
       "      <td>8.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3611</th>\n",
       "      <td>20082.0</td>\n",
       "      <td>8.92</td>\n",
       "      <td>9.01</td>\n",
       "      <td>8.70</td>\n",
       "      <td>8.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3612 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   open   high    low  close\n",
       "0     14613.0  10.79  10.79  10.63  10.64\n",
       "1     14614.0  10.60  10.71  10.21  10.31\n",
       "2     14615.0  10.27  10.52  10.11  10.34\n",
       "3     14616.0  10.18  10.41  10.01  10.10\n",
       "4     14617.0  10.01  10.20  10.01  10.20\n",
       "...       ...    ...    ...    ...    ...\n",
       "3607  20076.0   9.31   9.49   9.26   9.43\n",
       "3608  20077.0   9.38   9.52   9.29   9.34\n",
       "3609  20080.0   9.32   9.32   8.90   8.94\n",
       "3610  20081.0   9.00   9.00   8.87   8.92\n",
       "3611  20082.0   8.92   9.01   8.70   8.91\n",
       "\n",
       "[3612 rows x 5 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 拟合开始，数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b8602b8f20>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "#拟合开始\n",
    "#\n",
    "training_set = dataf1.iloc[0:2048, 4:5].to_numpy()#要提取一列数据，否则会出错\n",
    "test_set     = dataf1.iloc[3626 - 300:, 4:5].to_numpy()\n",
    "#print(training_set)\n",
    "plt.plot(training_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将数据归一化，范围是0到1\n",
    "sc  = MinMaxScaler(feature_range=(0, 1))\n",
    "training_set_scaled = sc.fit_transform(training_set)\n",
    "testing_set_scaled  = sc.transform(test_set) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取前 n_timestamp 天的数据为 X；n_timestamp+1天数据为 Y。\n",
    "def data_split(sequence, n_timestamp):\n",
    "    X = []\n",
    "    y = []\n",
    "    for i in range(len(sequence)):\n",
    "        end_ix = i + n_timestamp\n",
    "        \n",
    "        if end_ix > len(sequence)-1:\n",
    "            break\n",
    "            \n",
    "        seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
    "        X.append(seq_x)\n",
    "        y.append(seq_y)\n",
    "    return array(X), array(y)\n",
    "\n",
    "X_train, y_train = data_split(training_set_scaled, n_timestamp)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练数据重整"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train          = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
    "\n",
    "X_test, y_test   = data_split(testing_set_scaled, n_timestamp)\n",
    "X_test           = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\76496\\AppData\\Roaming\\Python\\Python312\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:200: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)          │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m50\u001b[0m)          │        \u001b[38;5;34m10,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │        \u001b[38;5;34m20,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m51\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#五、构建模型\n",
    "# 建构 LSTM模型\n",
    "model_type=2\n",
    "if model_type == 1:\n",
    "    # 单层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu',\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(units=1))\n",
    "if model_type == 2:\n",
    "    # 多层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu', return_sequences=True,\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(LSTM(units=50, activation='relu'))\n",
    "    model.add(Dense(1))\n",
    "if model_type == 3:\n",
    "    # 双向 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(Bidirectional(LSTM(50, activation='relu'),\n",
    "                            input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(1))\n",
    "\n",
    "model.summary()  # 输出模型结构"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型编译"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=tf.keras.optimizers.Adam(0.001),\n",
    "              loss='mean_squared_error')  # 损失函数用均方误差\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - loss: 0.1556 - val_loss: 0.0057\n",
      "Epoch 2/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0196 - val_loss: 0.0023\n",
      "Epoch 3/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0033 - val_loss: 5.2851e-04\n",
      "Epoch 4/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0024 - val_loss: 5.2608e-04\n",
      "Epoch 5/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0022 - val_loss: 5.4771e-04\n",
      "Epoch 6/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0021 - val_loss: 5.3390e-04\n",
      "Epoch 7/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0020 - val_loss: 5.3041e-04\n",
      "Epoch 8/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0020 - val_loss: 5.1913e-04\n",
      "Epoch 9/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0019 - val_loss: 5.0951e-04\n",
      "Epoch 10/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0018 - val_loss: 5.0295e-04\n",
      "Epoch 11/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0018 - val_loss: 5.1494e-04\n",
      "Epoch 12/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0017 - val_loss: 5.1893e-04\n",
      "Epoch 13/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0017 - val_loss: 5.2678e-04\n",
      "Epoch 14/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0016 - val_loss: 5.3795e-04\n",
      "Epoch 15/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0016 - val_loss: 5.5631e-04\n",
      "Epoch 16/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0015 - val_loss: 5.7500e-04\n",
      "Epoch 17/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0015 - val_loss: 5.8882e-04\n",
      "Epoch 18/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0015 - val_loss: 6.0315e-04\n",
      "Epoch 19/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0014 - val_loss: 6.0557e-04\n",
      "Epoch 20/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0014 - val_loss: 5.9557e-04\n",
      "Epoch 21/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0013 - val_loss: 5.6715e-04\n",
      "Epoch 22/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0013 - val_loss: 5.3068e-04\n",
      "Epoch 23/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0012 - val_loss: 4.8170e-04\n",
      "Epoch 24/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0012 - val_loss: 4.3501e-04\n",
      "Epoch 25/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0011 - val_loss: 4.0611e-04\n",
      "Epoch 26/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0010 - val_loss: 3.9472e-04\n",
      "Epoch 27/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0010 - val_loss: 3.8661e-04\n",
      "Epoch 28/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 9.9292e-04 - val_loss: 3.7996e-04\n",
      "Epoch 29/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 9.7349e-04 - val_loss: 3.7546e-04\n",
      "Epoch 30/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 9.5785e-04 - val_loss: 3.7105e-04\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)          │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m50\u001b[0m)          │        \u001b[38;5;34m10,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │        \u001b[38;5;34m20,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m51\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">91,955</span> (359.20 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m91,955\u001b[0m (359.20 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">61,304</span> (239.47 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m61,304\u001b[0m (239.47 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#七、训练模型\n",
    "history = model.fit(X_train, y_train,\n",
    "                    batch_size=64,\n",
    "                    epochs=n_epochs,\n",
    "                    validation_data=(X_test, y_test),\n",
    "                    validation_freq=1)  # 测试的epoch间隔数\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输出训练结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/+c9/6txzzw34+fDDDzV06FCtWrVKv/71rzV9+nRlZmbq4YcfliQVFhbqRz/6ke6++26df/752r17t+69915JrfU/b948ffvb39YFF1wg0zR16623RlXG3hjmCTClanV1tTwej2XHMwxDGet/o/qX18q4/BuyzZxn2bFPZIZhKD8/X4cOHWKm3jBQb+GjziLjq7ddu3YpJycn0cXpU+x2u6XfMyeLhoYGORyOLv+d2u125eXl9bo/LSndMHzdPY109wAAkAiElG7YfJO5MXAWAICEIKR0w9eSYjJwFgCAhCCkdMPGwFkAABKKkNINunsAAIhOuHPGdEZI6YbBPCkATmCxnt8C8Hq9hJRYYTI3ACeqzMxM1dXVEVQQM16vV3V1dRoyZEhUx+HZPd0wHDy7B8CJyW63KysrS/X19YkuSp+RlpampqamRBejT8nKypLD4dDRo0cjPgYhpRu2zLZnJTS4ZZpm1E1WAJBMUlNTmdAtREweGBkrvjfp7umGf0yK6ZWaGhNbGAAATkKElG4Y6RmS0VY93OEDAEDcEVK6YRiGlOFoXWCuFAAA4i6skOJ7pPP111+v3/zmNyH3zVVWVur666/vsn7nzp26/fbbdeONN+rll18Opyjx4Q8pDJ4FACDeQg4pHo9HpaWlKigo0PLly1VeXq5Nmzb1ul9VVZWWL1+u48ePB6yvra1VaWmpSkpKtHTpUm3ZskU7duwI+wPElKMtpNDdAwBA3IUcUrZv3y6Xy6UFCxbI6XRqzpw52rhxY6/7LV++XBdffHGX9Vu2bNGAAQM0a9Ys5efna/bs2SEdL64y2m5D5knIAADEXci3IO/bt09jx45Venq6JGnUqFEqLy/vdb+77rpLhmFo9erVXY43ceJE/y1KhYWFWrNmTY/H8ng88ng8/mXDMORoa+2w8hZh37GMjEyZktTg5hbkEPjrjboKC/UWPuosMtRbZKi3yFhRbyGHFLfbrby8vICT22w21dfXKzs7u9v9hgwZoqqqqi7rXS6Xhg8f7l92OByqqanpsQzr1q3Tiy++6F8uKChQaWlpQLmslDFggNySctLs6pefH5NznIicTmeii9AnUW/ho84iQ71FhnqLTDT1FnJIsdlsstvtAeuimYEvJSVFqantpw/lWDNnztSMGTP8y750Vl1drebm5ojKEYxhGHI6nWpo6w2rPVyp+kOHLDv+icpXb5WVlUx4FAbqLXzUWWSot8hQb5Hpqd5SU1NDamAIOaRkZ2frwIEDAevcbndA0AhHdna2amtrwzqW3W7vEpR8YnLhtN3dY7pdXJhhME2T+ooA9RY+6iwy1FtkqLfIRFNvIQ+cLSws1CeffOJfrqqqksfj6bGrpydjxowJON7evXuVm5sb0bFihuf3AACQMCGHlPHjx8vlcmnz5s2SpPXr12vSpEmy2WxyuVxhd7dMnjxZu3bt0o4dO9TS0qINGzaoqKgovNLHmJHOZG4AACRKyH01KSkpWrhwocrKyrR69Wp5vV4tXrxYkrRo0SItWLBAxcXFIZ84JydH8+fP17Jly5SZmamMjAzddNNNYX+AmGprSTHdtKQAABBvYQ0oKS4uVllZmXbv3q1x48b5n6D56KOP9rjf4MGDtXbt2i7rp0+frqKiIpWXl2vChAnKzMwMpzix55txlnlSAACIu7BHvebm5lo6dsTpdCbvbV2+ydyYcRYAgLjjAYM94dk9AAAkDCGlB4aDgbMAACQKIaUnGdyCDABAohBSetKhu8f0ehNbFgAATjKElJ44Otxt1NiQuHIAAHASIqT0xJ4m2dqqiC4fAADiipDSA8MwOoxLYfAsAADxREjpDc/vAQAgIQgpvfENnmVCNwAA4oqQ0psM5koBACARCCm9aQspJt09AADEFSGlF4b/+T2EFAAA4omQ0hsHd/cAAJAIhJTe8JBBAAASgpDSG+ZJAQAgIQgpvaElBQCAhCCk9MbRdncP86QAABBXhJTeZDDjLAAAiUBI6YVBdw8AAAlBSOkNLSkAACQEIaU3TIsPAEBCEFJ645vMjYGzAADEFSGlN76WlKZGmd6WxJYFAICTCCGlN74xKRLjUgAAiCNCSi8Mu11KTW1dIKQAABA3hJRQ+Lp8eBIyAABxQ0gJBc/vAQAg7ggpoWBCNwAA4o6QEgpaUgAAiDtCSija5koxaUkBACBuCCkh8D+/hwndAACIG0JKKJgaHwCAuCOkhIKBswAAxB0hJRS+gbPMkwIAQNwQUkLhe8ggLSkAAMQNISUUbd09JmNSAACIG0JKKDJoSQEAIN4IKSEwuLsHAIC4I6SEwsE8KQAAxBshJRTcggwAQNwRUkLBmBQAAOKOkBIK3y3IniaZzc2JLQsAACcJQkoo0h3trxtpTQEAIB4IKSEwUlMle1rrAoNnAQCIC0JKqBg8CwBAXBFSQsVcKQAAxBUhJVQ8vwcAgLhKDWfj/fv367HHHlNlZaWmTp2qefPmyTCMHvfZuXOnnnzySdXW1mrmzJmaMWOG/73f//732rBhg5qamjRp0iR9+9vfVr9+/SL7JLHme36P262ePzEAALBCyC0pHo9HpaWlKigo0PLly1VeXq5Nmzb1uE9tba1KS0tVUlKipUuXasuWLdqxY4ek1vCyefNmLVmyRKWlpfJ4PFq1alVUHyam/HOl0N0DAEA8hBxStm/fLpfLpQULFsjpdGrOnDnauHFjj/ts2bJFAwYM0KxZs5Sfn6/Zs2f79/n000911llnaejQoXI6nSopKdGhQ4ei+zQxZDBwFgCAuAq5u2ffvn0aO3as0tPTJUmjRo1SeXl5r/tMnDjR3yVUWFioNWvWSJJGjhypp59+WpdccokcDodef/11nXHGGT0ez+PxyOPx+JcNw5Cj7bk6vXU7hcN3rIBjto1JMRrclp7rRBK03tAr6i181FlkqLfIUG+RsaLeQg4pbrdbeXl5ASe32Wyqr69XdnZ20H1cLpeGDx/uX3Y4HKqpqZEknXnmmcrPz9ett94qSRozZoyuvPLKHsuwbt06vfjii/7lgoIClZaWBpTLSk6n0//66KA81UnKSrGpf35+TM53ouhYbwgd9RY+6iwy1FtkqLfIRFNvIYcUm80mu90esC4tLU1NTU3d7pOSkqLU1NSg22/btk3V1dVasWKFcnJytGrVKpWVlekHP/hBt8frPPDWl86qq6vVbOF09YZhyOl0qrKyUqZpSpK8zV5JUv2RarmTuFsqkYLVG3pHvYWPOosM9RYZ6i0yPdVbampqSA0MIYeU7OxsHThwIGCd2+0OCCHB9qmtrQ26/datWzVt2jQNHTpUknTdddfpuuuu0/Hjx5WVlRX0eHa7vUtQ8onFhWOapv+4pm9MitvFRdqLjvWG0FFv4aPOIkO9RYZ6i0w09RbywNnCwkJ98skn/uWqqip5PJ5uu3qk1i6cjvvs3btXubm5kqSWlhYdO3bM/97Ro0clSV6vN+TCx5XvFmQGzgIAEBchh5Tx48fL5XJp8+bNkqT169dr0qRJstlscrlcQbtbJk+erF27dmnHjh1qaWnRhg0bVFRUJEk67bTT9Oc//1l/+tOftGnTJq1YsUJjx45N2nlSDN9kbjy7BwCAuAi5uyclJUULFy5UWVmZVq9eLa/Xq8WLF0uSFi1apAULFqi4uDhgn5ycHM2fP1/Lli1TZmamMjIydNNNN0mSLr/8ctXU1Oill15SXV2dxo4d638vKWUw4ywAAPEU1oyzxcXFKisr0+7duzVu3Djl5ORIkh599NFu95k+fbqKiopUXl6uCRMmKDOz9cs+LS1NN9xwg2644YYoih9HPLsHAIC4CiukSFJubq5/XEmonE5n3791i5YUAADiigcMhoqWFAAA4oqQEqq2mW3V3Cyzw6y3AAAgNggpoUp3tL+mywcAgJgjpITISEmR0lqfW0SXDwAAsUdICYeDwbMAAMQLISUc6e1T4wMAgNgipITD35JCSAEAINYIKeHg+T0AAMQNISUczJUCAEDcEFLCYDBwFgCAuCGkhMPXkuImpAAAEGuElHDw/B4AAOKGkBIOxqQAABA3hJRw+FpSmCcFAICYI6SEo+0hg2Yj3T0AAMQaISUMRgYzzgIAEC+ElHAwcBYAgLghpITDP3CWkAIAQKwRUsLhYOAsAADxQkgJh+8pyI1umaaZ2LIAAHCCI6SEw9eS0tIieZoSWxYAAE5whJRwpGe0v2ZcCgAAMUVICYNhs7V3+TDrLAAAMUVICZeDhwwCABAPhJRwcRsyAABxQUgJl39CN7p7AACIJUJKuNru8DFpSQEAIKYIKeFK5/k9AADEAyElTIajfUI3AAAQO4SUcPEkZAAA4oKQEi6ehAwAQFwQUsLl4O4eAADigZASrrbuHpPJ3AAAiClCSrjo7gEAIC4IKWEyMnh2DwAA8UBICRd39wAAEBeElHD5Bs4yTwoAADFFSAlXBk9BBgAgHggp4eowcNY0zcSWBQCAExghJVy+lhTTKzU1JrYsAACcwAgp4UrPkAyj9TWDZwEAiBlCSpgMw2hvTWGuFAAAYoaQEokMpsYHACDWCCmRoCUFAICYI6REgllnAQCIOUJKJNomdDNpSQEAIGYIKZFgQjcAAGIuNZyN9+/fr8cee0yVlZWaOnWq5s2b13q3Sw927typJ598UrW1tZo5c6ZmzJjRZZsVK1YoJydHN9xwQ3ilTxAjI1OmRHcPAAAxFHJLisfjUWlpqQoKCrR8+XKVl5dr06ZNPe5TW1ur0tJSlZSUaOnSpdqyZYt27NgRsM3777+vHTt26Jvf/GZEHyAhHO2zzgIAgNgIOaRs375dLpdLCxYskNPp1Jw5c7Rx48Ye99myZYsGDBigWbNmKT8/X7Nnzw7Yp6mpSU8//bTmzp2rrKysyD9FvKXzJGQAAGIt5O6effv2aezYsUpPT5ckjRo1SuXl5b3uM3HiRH+XUGFhodasWeN//8UXX1RTU5NSUlK0Y8cOnX766T12H3k8Hnk8Hv+yYRhyOBz+11bxHau7YxqOtu6eRrel5+3reqs3BEe9hY86iwz1FhnqLTJW1FvIIcXtdisvLy/g5DabTfX19crOzg66j8vl0vDhw/3LDodDNTU1kqTPP/9cf/jDH1RYWKjPP/9cf/zjHzVo0CD94Ac/6PYDrVu3Ti+++KJ/uaCgQKWlpQHlspLT6Qy6vn6IU19IyjBNDcrPj8m5+7Lu6g09o97CR51FhnqLDPUWmWjqLeSQYrPZZLfbA9alpaWpqamp231SUlKUmpoadPtNmzbplFNO0b333iu73a7LL79cN998s/7xj3+oqKgo6PE6D7z1hZnq6mo1NzeH+lF6ZRiGnE6nKisrgz7p2NvU2prTcLRGhw4dsuy8fV1v9YbgqLfwUWeRod4iQ71Fpqd6S01NDamBIeSQkp2drQMHDgSsc7vdASEk2D61tbVBtz9y5IgmTpzoDz4Oh0P5+fmqqqrq9nh2u71LUPKJxYVjmmbw47bdgmw2uLlgg+i23tAj6i181FlkqLfIUG+RiabeQh44W1hYqE8++cS/XFVVJY/H021XjySNGTMmYJ+9e/cqNzdXkjRw4MCAVhiv16sjR47ErOvGUhkMnAUAINZCDinjx4+Xy+XS5s2bJUnr16/XpEmTZLPZ5HK5gna3TJ48Wbt27dKOHTvU0tKiDRs2+Ltyzj//fL333nt66623dOTIEa1Zs0bNzc0aN26cRR8thjK4BRkAgFgLubsnJSVFCxcuVFlZmVavXi2v16vFixdLkhYtWqQFCxaouLg4YJ+cnBzNnz9fy5YtU2ZmpjIyMnTTTTdJkoYOHarbb79dzz//vA4ePCin06lFixb579ZJajy7BwCAmAtrxtni4mKVlZVp9+7dGjdunHJyciRJjz76aLf7TJ8+XUVFRSovL9eECROUmZnpf+/ss8/W2WefHWHRE8jR/hRk0+uVYePpAgAAWC2skCJJubm5/nEloXI6nSfWrVsZ7UFLTQ2BywAAwBI0AUTCnib5Wk94yCAAADFBSImAYRgMngUAIMYIKZFi8CwAADFFSIkUT0IGACCmCCmRYkI3AABiipASKf/U+IQUAABigZASIYOBswAAxBQhJVJ09wAAEFOElEj5Bs420pICAEAsEFIi5W9JIaQAABALhJRI+cek0N0DAEAsEFIi5fDd3UNLCgAAsUBIiVQ6A2cBAIglQkqEDGacBQAgpggpkfI/u4eQAgBALBBSIsXAWQAAYoqQEim6ewAAiClCSqR83T2NDTK9LYktCwAAJyBCSqR83T2S1NCQuHIAAHCCIqREyLDbpZTU1gXGpQAAYDlCSjQc3OEDAECsEFKiwYRuAADEDCElGtzhAwBAzBBSopFBSAEAIFYIKdHI8D1kkO4eAACsRkiJAs/vAQAgdggp0chg4CwAALFCSImG/yGDhBQAAKxGSIkGA2cBAIgZQko0/N09hBQAAKxGSIlG28BZs5GQAgCA1Qgp0WDgLAAAMUNIiYLhH5NCSAEAwGqElGjwgEEAAGKGkBINunsAAIgZQko0fN09DJwFAMByhJRo+FpSmppktrQktiwAAJxgCCnR8IUUiXEpAABYjJASBSPVLtnTWhe4wwcAAEsRUqLF4FkAAGKCkBKtDG5DBgAgFggp0eJJyAAAxAQhJVoOnoQMAEAsEFKild7akmIyJgUAAEsRUqJk0JICAEBMEFKilUFIAQAgFggp0WLgLAAAMRFWSNm/f7/uvvtuXX/99frNb34j0zR73Wfnzp26/fbbdeONN+rll18Ouk1zc7PuvPNOffTRR+EUJznwJGQAAGIi5JDi8XhUWlqqgoICLV++XOXl5dq0aVOP+9TW1qq0tFQlJSVaunSptmzZoh07dnTZ7ve//70OHDgQduGTApO5AQAQEyGHlO3bt8vlcmnBggVyOp2aM2eONm7c2OM+W7Zs0YABAzRr1izl5+dr9uzZXfY5dOiQNmzYoLy8vMg+QaK1jUkxaUkBAMBSqaFuuG/fPo0dO1bp6emSpFGjRqm8vLzXfSZOnCjDMCRJhYWFWrNmTcA2TzzxhL7+9a/r/fff77UMHo9HHo/Hv2wYhhxt3S2+c1jBd6xQjmk4smRKUoPb0jL0ReHUG9pRb+GjziJDvUWGeouMFfUWckhxu90BrR2GYchms6m+vl7Z2dlB93G5XBo+fLh/2eFwqKamxr/8+uuvy+Vy6Wtf+1pIIWXdunV68cUX/csFBQUqLS2NWSuM0+nsdRv30GH6XJK92SNnfn5MytHXhFJv6Ip6Cx91FhnqLTLUW2SiqbeQQ4rNZpPdbg9Yl5aWpqampm73SUlJUWpqatDta2trtWbNGt1zzz1KSUkJqQwzZ87UjBkz/Mu+dFZdXa3m5uZQP0qvDMOQ0+lUZWVlr4ODTXeDJMlTX6tDhw5ZVoa+KJx6QzvqLXzUWWSot8hQb5Hpqd5SU1NDamAIOaRkZ2d3GdzqdrsDQkiwfWpra4Nu/+tf/1pTp07V6NGjQy2C7HZ7l6DkE4sLxzTN3kNKekbrC7eLi7dNKPWGrqi38FFnkaHeIkO9RSaaegs5pBQWFgYMeq2qqpLH4+m2q0eSxowZo61bt/qX9+7dq9zcXEnSG2+8IYfDof/93/+VJDU0NOjBBx/UrFmzdOWVV4b7ORKHydwAAIiJkEPK+PHj5XK5tHnzZk2ZMkXr16/XpEmTZLPZ5HK5lJaW1qVVZfLkyXr66ae1Y8cOjR8/Xhs2bFBRUZEkaeXKlQHb/uxnP9Pll1+uM888M/pPFU++eVKaPTI9HhndtPQAAIDwhBxSUlJStHDhQpWVlWn16tXyer1avHixJGnRokVasGCBiouLA/bJycnR/PnztWzZMmVmZiojI0M33XSTJGnw4MEB29rtdvXv319ZWVlRfqQ4a3vAoKTW1hRCCgAAlgg5pEhScXGxysrKtHv3bo0bN045OTmSpEcffbTbfaZPn66ioiKVl5drwoQJyszMDLqdL/D0NUZKipSWLjU1tk6N3y8n0UUCAOCEEFZIkaTc3Fz/uJJQOZ3OE/vWrQxHW0hhXAoAAFbhAYNWYPAsAACWI6RYgSchAwBgOUKKFRxtz+/hIYMAAFiGkGIFf0sK3T0AAFiFkGIBg+4eAAAsR0ixgoOBswAAWI2QYgXfhG5uQgoAAFYhpFjB35JCdw8AAFYhpFiBeVIAALAcIcUKbQNnTVpSAACwDCHFAoaDW5ABALAaIcUKvluQmcwNAADLEFKswJgUAAAsR0ixAvOkAABgOUKKFdLbZ5w1TTOxZQEA4ARBSLGCryWlpUVq9iS2LAAAnCAIKVZIz2h/zeBZAAAsQUixgGGzdejyYVwKAABWIKRYxcGTkAEAsBIhxSoZPGQQAAArEVKswlwpAABYipBiFZ7fAwCApQgpVqElBQAASxFSLGJkMHAWAAArEVKs4uAhgwAAWImQYhW6ewAAsBQhxSp09wAAYClCilXant9j0pICAIAlCClWSWcyNwAArERIsYjhexIy3T0AAFiCkGKVDB4wCACAlQgpVsmgJQUAACsRUqzioCUFAAArEVKs0uEpyKZpJrYsAACcAAgpVvF195heqakpsWUBAOAEQEixSnqGZBitrxmXAgBA1AgpFjEMo0OXDyEFAIBoEVKs5OvyaWTwLAAA0SKkWImWFAAALENIsRITugEAYBlCipXaQorJwFkAAKJGSLGS7/k9PGQQAICoEVIsZPinxiekAAAQLUKKlfxjUujuAQAgWoQUK9GSAgCAZQgpVuIWZAAALENIsZKDu3sAALBKajgb79+/X4899pgqKys1depUzZs3r3U6+B7s3LlTTz75pGprazVz5kzNmDHD/96f//xnrV27VnV1dTr99NP13e9+VwMGDIjskyQDunsAALBMyC0pHo9HpaWlKigo0PLly1VeXq5Nmzb1uE9tba1KS0tVUlKipUuXasuWLdqxY4ckadeuXXrhhRd0yy23aOXKlfJ4PPrNb34T1YdJNIPJ3AAAsEzIIWX79u1yuVxasGCBnE6n5syZo40bN/a4z5YtWzRgwADNmjVL+fn5mj17tn+fiooKfetb39IZZ5yhgQMH6qKLLtLu3buj+zSJ5qAlBQAAq4Tc3bNv3z6NHTtW6enpkqRRo0apvLy8130mTpzo7xIqLCzUmjVrJElTp04N2LaiokJOp7PH43k8Hnk8Hv+yYRhytI0D6a3bKRy+Y4V9TH93j8vS8vQVEdfbSY56Cx91FhnqLTLUW2SsqLeQQ4rb7VZeXl7AyW02m+rr65WdnR10H5fLpeHDh/uXHQ6HampqumxXV1enP//5z/re977XYxnWrVunF1980b9cUFCg0tLSgHJZqbfQ1JnH61GlJKOxQfn5+TEpU18Qbr2hFfUWPuosMtRbZKi3yERTbyGHFJvNJrvdHrAuLS1NTU1N3e6TkpKi1NTUXrd/6qmnNHbsWJ199tk9lqHzwFtfOquurlZzc3NInyMUhmHI6XSqsrJSpmmGvJ9ZV9/6r9ulioMHZdhOrpunIq23kx31Fj7qLDLUW2Sot8j0VG+pqakhNTCEHFKys7N14MCBgHVutzsghATbp7a2tsftN27cqI8//lg/+clPei2D3W7vEpR8YnHhmKYZXkhJd/h2lNnobu/+OcmEW29oRb2FjzqLDPUWGeotMtHUW8h/6hcWFuqTTz7xL1dVVcnj8XTb1SNJY8aMCdhn7969ys3N9S9/+umnevbZZ/X9739f/fv3D7PoSSgtTfK1njB4FgCAqIQcUsaPHy+Xy6XNmzdLktavX69JkybJZrPJ5XIF7W6ZPHmydu3apR07dqilpUUbNmxQUVGRJOno0aMqLS3V17/+dZ166qlqaGhQQ0ODRR8rMQzD6DDrLCEFAIBohNzdk5KSooULF6qsrEyrV6+W1+vV4sWLJUmLFi3SggULVFxcHLBPTk6O5s+fr2XLlikzM1MZGRm66aabJElbt27VsWPH9Pzzz+v555/377N27VoLPlYCZWRKruO0pAAAEKWwZpwtLi5WWVmZdu/erXHjxiknJ0eS9Oijj3a7z/Tp01VUVKTy8nJNmDBBmZmt4zSuuOIKXXHFFVEUPUk52m9DBgAAkQsrpEhSbm5uwLiSUDidzpPn1i0eMggAgCVOrntk4yHD95BBunsAAIgGIcVq/uf30JICAEA0CCkWM3gSMgAAliCkWI2WFAAALEFIsZrv7h7mSQEAICqEFKvR3QMAgCUIKVbz391Ddw8AANEgpFjM6Nc6wZ3K98r0tiS2MAAA9GGEFKudfraUmSV9flj68L1ElwYAgD6LkGIxIz1DxgXTJEnev2xIcGkAAOi7CCkxYHz1CsmwSR9/ILN8b6KLAwBAn0RIiQFj4GDprC9LksyNLye4NAAA9E2ElBixXfw1SZL51iaZdbUJLg0AAH0PISVWvjRBGnmq5GmSueV/E10aAAD6HEJKjBiGIcPXmvL6H2U2Nye4RAAA9C2ElBgyzr1Q6neKdPSIzO1vJro4AAD0KYSUGDLsdhkX/askyeR2ZAAAwkJIiTFjyr9KKanS7l0yP/sk0cUBAKDPIKTEmHHKABnnXiBJMv/y+wSXBgCAvoOQEgfGJW0DaP+2VebRmgSXBgCAvoGQEgfGqEKpcLzU0ixz8yuJLg4AAH0CISVO/JO7bX5VpqcpwaUBACD5EVLi5ayvSLmDpLpjMt/5a6JLAwBA0iOkxImRkiLjoiskSeafN8g0zcQWCACAJEdIiSPjwmlSWppU/pn0z48SXRwAAJIaISWOjKx+Mr48VZLk5XZkAAB6REiJM+PiGa0v3n9H5ueHE1sYAACSGCElzoyhI6UJZ0qmV+brf0h0cQAASFqElATw34685TWZDe4ElwYAgORESEmEiedIg4dK7uMy33w90aUBACApEVISwLDZZExtHZtibtwg0+tNcIkAAEg+hJQEMUqmSo5MqfKg9NH2RBcHAICkQ0hJECMjU0bJpZK4HRkAgGAIKUF8VOXSBwePxfw8xtQrJMOQPtou81B5zM8HAEBfQkjppKHZq0e2Vujf17ynx9+tlMvTErNzGXlOqahYUuvYFAAA0I6Q0kmL19QkZ6ZMSX/4f1/oey9/pr8drI/Z+fy3I2/bKPN47M4DAEBfQ0jpJCstRbd9ZahW/p8zNSTbrs9dzfrxpnL99I0KHWtotv6E4yZJw0dLTY0y33jN+uMDANBHEVK6cd7oXP18xqm6cnyubIb01321+u7Ln+n1PccsfYKxYRjttyO//geZLbHrXgIAoC8hpPQgI9Wm688erJ9MH6XR/dNV19iiFW8e0pLXy1VV77HsPMZ5U6TsftKRKumDty07LgAAfRkhJQRfGujQT/91tK4typPdZmj7oeP63h/2aMOuGrV4o29VMdLSZVx4mSTJ+xcG0AIAIBFSQpZqMzR74kCtuGK0JuQ51NBs6qn3qnTXn/Zp39HGqI9vXHS5lJIi/fMjmfv3RF9gAAD6OEJKmIbnpGvZpSP1neIhyrTb9M8jDbrjlc+05h/V8rREPr29MWCgjHNKJEkmrSkAACg10QXoi2yGocu+NEDnDsvWL989rHfK6/XCh0e0dV+dbvmyU+PzMiM6rnHx12S+81eZ72xWS6NbxsDBUu5gGYMGSwPzWl9nZln8aQAASE6ElCgMzLTrPy4cpm376/T43w6rvLZJd/9pv/51bH/NPSNP/dJTwjqeceo46UsTpE92Su9tk2+0S8CoF0dWa2AZOLg1xAzM84cZDRosZefIMAyrPiIAAAlDSImSYRgqGZWjM5xZ+tXfq/SXPcf0x38e1Z8+PaoiZ5b+ZVSOiodnKzsttMBiu+VH0v/7UOaRKulItcwjh6Uj1VJNlVRfJ7mPS+XHpfK9wUNMWprkyJZSUyW7XUrt8GNv/9foZr1S7ZLN1vpjtP2bktL+2maTbCn+10aHdWZKitwHB8l77FjrdP++7VJSOu2X0vVYKTbJ6Lg+sAyGjZ5JADjZEFIs0i89Rbd+JV8Xjs7Rr7dX6bMvGvVexXG9V3FcqTbprPwslYxsDSxZPQQWIzNLOuvLCtYWYja4pZrqwPBypEpmTeu/OlojNTVJTTW9lteqmV46H+dzi44bVEB46Rxo2pYNI2jICQxZtg7bpnTazgjY17B1Wm903r/t3IYRuI1htG1rtL8OOEb7esNmU90pp8hbVy/TMNres3Xar/210fEcvvXByug/j69+DAWtj87ltvn267hPx+X27Wm1AxBLhBSLnZmfpRX5BTpwrFFb99dp675a7T/WpHcPHte7B48r1Wbo7KFZKhnZT8XDs5VpD71LyMhwSENHSkNHBg8xHo/0xedSg1tq9rT+eDz+12aH1/73Oq9rbpa8LZLX6/8xOy3L9EotLe2v/e+1yJ6SIk9jY+v7HbfrsE3Avx23643vGHFk3bR9PZ/jaJjbJ41g4azLa6M9yHUJSh0DmdEpWHWzvi2oVWc41NLU1BbsArc1gqzrHPaChsiA8/ccFhUsLPrP12nZZguyLkVGt+cPbf/uyx8kMBuGlGJTS7pdZu3R1nrrLux2OA9BFIlkmGFMn7p//3499thjqqys1NSpUzVv3rxeL+CdO3fqySefVG1trWbOnKkZM2aE9F44qqur5fFYOLmaYSg/P1+HDh2yZHbZ/UcbtXV/rd7YV6fy2ib/entbYPmXUTmaPCwrrMCSjKKtN7NjCPJ2+jE7h53OoccrmWb7uqDH6Hp8M+B9s21fs8N2Ztu5zfZl//HNINu0rTPNDscxO5y74z7t22ZkpKvhuEtmx+OaZqftOx2j4zkCPl+I6wI+Y6ftzPiGQSSxYCEp5CDauXUvWItkp2DasXXS163cUwtg51bTnlpQQwqiti6f2UixqX//ATp67JhMGTIMtYdFdQymRjfr1b6szvt2eK+n7dre6rIcsH3H5c77+N6TOmzc4byd1nX8/3/AIBkp4X8/9fSdYLfblZeX1+sxQm5J8Xg8Ki0tVVFRkW677Tb96le/0qZNm/TVr361231qa2tVWlqqr33tayopKdGKFSs0evRoTZw4scf3TjQj+6drZP88XT1pkPYfa9Ib+1oDS0Vdk94ur9fb5fVKSzF0ztDWLqGzhmYpLaXn8NfTu6Zav2s6vjbb/v5ufd36hinfa7P9dafjt1+7RuC6INe/YRhyNTXL7fF2uSCDZdngn6GtGyG14/tGwDE67mcElCP8v/iS4W9EwzA0KJpw17aPGbAuyHZB3w/ct+N+3rbQYrZ4JXmllrb/X73etjDVFvJMU6Y38D3TF8K8Zvuy7z3T7LDcup1p+o7T+XXg+16vWgOhKWVnZ6m2tk7qcD7f9lLg/h3fM9vK6/vA/vXetv06bS+vKVMdj6X2MKn243c+j/+9gO3UHjDV/trw/9vxWF7/stEplBpt64y2bQyvt8Ny6/83trYyGmbba9/73hbZvF4ZMlv/uzFNGTJlM00Z8sow1WHZ9L9vmKZs3tZtbKandbntGLGW6NZDU1JNp+WTie2/npVOGZCQc4ccUrZv3y6Xy6UFCxYoPT1dc+bM0dNPP91jSNmyZYsGDBigWbNmyTAMzZ49Wxs3btTEiRN7fO9EZRiGRvVP16j+eZp7xiDtO9qoN/bV6Y39tTpU59GbB+r15oG+/iTkfya6AAH8waqHkGR0/itCwYNQd/v7dP7F5Q+EHd7tGB47bid93OOxTkw2C47hsOAYIerwxyna+arF5mtEUYd/ZQass6ktAPn3Mf3bdf7XJrM9MLX9qO09+YKVJLWFJf+/agtevnDVIWj5/kLruGz4AmTbfgqybJimUmw2eVtaZEodjhW4v9rK03qZtIXODuvlX6/21+r4iyL4cdsvO7PTL4euywHn9J9D7X+ddti1u3061pUkXdPsVaImvwg5pOzbt09jx45Venq6JGnUqFEqLy/vdZ+JEyf6/8ItLCzUmjVren3vZGAYhkYPyNDoARm6pmiQPvuiUW/sq9XW/XWqtPC5QCe7YC0E3W50kkSDRAhocfO/NgJbort5z9+C3WEbGYZSbLbWFpGOx/UfyOi0HLi/7xzqeI72XQPiancteL3lFaOXLfytm75ls/P7nde3b+9vlGn7IvGagetlmvK2bezttCzDkNdryutrQTVby+J7wke4T/po+1r3NxgFClYHCUh6XZqGEY7/k3lK8ocUt9sd0H9kGIZsNpvq6+uVnZ0ddB+Xy6Xhw4f7lx0Oh2pqanp9rzsejydg7IlhGHI4HP7XVvEdK14DxgzD0JiBDo0Z6ND8swbL3ezt8fuyt+9b/y9no/UXZcdfqEa367p+XrPDL7HO5/b9cvT9r9n2y885ZIgqDx8Omgp6+93X3srQ/tdB+98e7S/a15ld1nUtY+Dn6a4swT5jd+ULOHeHfVrrPbBrSuq+NcdX54ZhaPDgwaqqqgooY+hdZF27lNWxLN0coGuXXqf9gnw5B18f2vVkJcMw5HQ6VVlZaelTyU90odabaQaGltbuLt9QqtbA07rcup3XNP0BqcX3uvP7Ums48u+rDkHJVIvp69Fq3cbrNf3hx9uhPL7jSh2DmRkQ3HzH9P2a8gU1/z7+D6ou67qEwrbfbf369VNdXV3g75FOv6e67Keu73duCAm6TU//33Re7uXy7/73Wej/3WTYI7uTz4rv0pBDis1mk91uD1iXlpampqambvaQUlJSlJqaGnT7nt7rzrp16/Tiiy/6lwsKClRaWhrS4JtIOJ3OmBz3RDd6+NBEF6FPGlgwvPeNEID/RiNDvSGeorneQg4p2dnZOnDgQMA6t9sdEDSC7VNbWxt0+57e607nO4B86ay6ulrNzc2hfpRe8VdaZKi3yFBv4aPOIkO9RYZ6i0xP9Zaammrt3T2FhYXauHGjf7mqqkoej6fbrh5JGjNmjLZu3epf3rt3r3Jzc3t9rzt2u71La45PLC4c0zdSH2Gh3iJDvYWPOosM9RYZ6i0y0dRbyMPrx48fL5fLpc2bN0uS1q9fr0mTJslms8nlcgVtyZg8ebJ27dqlHTt2qKWlRRs2bFBRUVGv7wEAAITckpKSkqKFCxeqrKxMq1evltfr1eLFiyVJixYt0oIFC1RcXBywT05OjubPn69ly5YpMzNTGRkZuummm3p9DwAAIKwZZyWppqZGu3fv1rhx45STkxPSPpWVlSovL9eECROUmZkZ8nuhSvYZZ08W1FtkqLfwUWeRod4iQ71FJq4zzvrk5ub2OnakM6fT2e3o3p7eAwAAJy8rpnwEAACwHCEFAAAkJUIKAABISoQUAACQlAgpAAAgKRFSAABAUiKkAACApBT2PCnJqLcHEybbcU901FtkqLfwUWeRod4iQ71FJli9hVqXYc84CwAAEA909wThdrv1wx/+UG63O9FF6VOot8hQb+GjziJDvUWGeouMFfVGSAnCNE199tlnPKMhTNRbZKi38FFnkaHeIkO9RcaKeiOkAACApERIAQAASYmQEoTdbtfs2bNlt9sTXZQ+hXqLDPUWPuosMtRbZKi3yFhRb9zdAwAAkhItKQAAICkRUgAAQFIipAAAgKTEHL+wxDPPPKNXX33VvzxkyBD9/Oc/T2CJcKKpq6vTXXfdpfvvv1+DBw+WxHUH67377rt69tln9fnnn+vUU0/VzTffrOHDh3OthaCurk4VFRXKz89XTk6OJcckpHSyf/9+PfbYY6qsrNTUqVM1b948GYaR6GIlvT179uiuu+7SuHHjJEk2G4103Qn2Zct117Pa2lqVlpaquro6YD3XXc+6+8LleguusrJSv/jFL/Tv//7vmjBhgp555hk9/vjj+vGPf8y11outW7fqqaeeUl5enioqKvSd73xHJSUlUV9r1HIHHo9HpaWlKigo0PLly1VeXq5NmzYlulhJr6WlRQcOHNCECROUlZWlrKwsORyORBcrKdXW1urBBx8M+LLluuvdz372M5WUlASs47rrme8Ld+7cufrlL3+pQYMG6fHHH+d668HBgwc1Z84cnX/++erfv7+mTZum3bt3c6314vjx43rmmWe0ZMkS/eQnP9HChQv13HPPWXKtEVI62L59u1wulxYsWCCn06k5c+Zo48aNiS5W0tu3b59M09SiRYt0zTXXaNmyZfr8888TXaykFOzLluuudwsXLtTll18esI7rrmfdfeFyvXXvnHPO0bRp0/zLFRUVcjqdXGu9cLvduu666zRy5EhJ0qhRo3T8+HFLrjVCSgf79u3T2LFjlZ6eLqm1osvLyxNcquR38OBBjRgxQrfddpseeeQRpaSk6Iknnkh0sZJSd1+2XHc9GzJkSJd1XHc96+kLl+utd83NzdqwYYOmTZvGtdaLQYMG6YILLpDUXm/FxcWWXGuMSenA7XYrLy/Pv2wYhmw2m+rr65WdnZ3AkiW3Cy64wH+BStKNN96oW265RS6XS5mZmQksWfIJ9mXLdRcZrrvQ+b44rrjiClVVVXG9heD5559XRkaGLrnkEqWmpnKthWDv3r1asmSJUlNTtWLFCr300ktRX2u0pHRgs9m6TN+blpampqamBJWob8rKypJpmjp69Giii9IncN1Zg+uuex2/cLneevePf/xDr732mm677Talpnb9W55rLbhRo0bpvvvu04gRI/SLX/zCkmuNkNJBdna2amtrA9a53e6gFynaPfvss9q2bZt/+dNPP5VhGBo4cGACS9V3cN1FhusuNJ2/cLneenb48GGVlZXpW9/6loYPHy6Jay1UhmGooKBAN998s/72t79Zcq1xVXZQWFgYMKinqqpKHo+HJtBejB49Ws8//7z69+8vr9erX/3qV7rooov8/ZDoGdddZLjuehfsC5frrXtNTU168MEHde655+rcc89VQ0ODpNYWAq617u3YsUPbt2/XtddeK0lKSUmRJA0bNizqa42Q0sH48ePlcrm0efNmTZkyRevXr9ekSZO4H74XU6ZM0cGDB/XQQw8pIyNDxcXFmjNnTqKL1Wdw3UWG665n3X3hnnbaaVxv3Xj//fd18OBBHTx4UH/5y1/861euXKmKigqutW4MHTpUDz30kPLz83XmmWfq+eef1xlnnKGzzz5bjz/+eFTXGk9B7uSdd95RWVmZHA6HvF6vFi9erBEjRiS6WDjBfOMb39DKlSv9k7lx3cFq77zzjv7rv/6ry/qVK1dq7969XG+w1Pvvv69nn31WNTU1Kioq0re+9S3l5ORE/buNkBJETU2Ndu/erXHjxlk2tS/QG647xBPXG+IlmmuNkAIAAJISnZAAACApEVIAAEBSIqQAAICkREgBAABJiZACAACSEiEFAAAkJUIKAABISoQUAACQlAgpAAAgKf1/hoV/nmrzQbMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "#plt.title('Training and Validation Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n"
     ]
    }
   ],
   "source": [
    "predicted_stock_price = model.predict(\n",
    "    X_test)                        # 测试集输入模型进行预测\n",
    "predicted_stock_price = sc.inverse_transform(\n",
    "    predicted_stock_price)  # 对预测数据还原---从（0，1）反归一化到原始范围\n",
    "real_stock_price = sc.inverse_transform(y_test)  # 对真实数据还原---从（0，1）反归一化到原始范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\python\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 9 (\t) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画出真实数据和预测数据的对比曲线\n",
    "plt.plot(real_stock_price, color='red', label='Stock Price')\n",
    "plt.plot(predicted_stock_price, color='blue', label='Predicted Stock Price')\n",
    "plt.title(stockname)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Stock Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型校核"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "均方误差: 0.09919\n",
      "均方根误差: 0.31495\n",
      "平均绝对误差: 0.21889\n",
      "R2: 0.85493\n"
     ]
    }
   ],
   "source": [
    "MSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)\n",
    "RMSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)**0.5\n",
    "MAE = metrics.mean_absolute_error(predicted_stock_price, real_stock_price)\n",
    "R2 = metrics.r2_score(predicted_stock_price, real_stock_price)\n",
    "\n",
    "print('均方误差: %.5f' % MSE)\n",
    "print('均方根误差: %.5f' % RMSE)\n",
    "print('平均绝对误差: %.5f' % MAE)\n",
    "print('R2: %.5f' % R2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
